From fe411af3dbed474d9e5b3cc1d509f66bd76f7319 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <johannes.ranke@jrwb.de>
Date: Fri, 28 Nov 2025 09:20:07 +0100
Subject: Example evaluation of D24_2014, improve docs

---
 docs/404.html                                      |   2 +-
 docs/articles/index.html                           |   2 +-
 docs/articles/web_only/mesotrione_parent_2023.html |  65 ++++++++++-----------
 docs/authors.html                                  |   6 +-
 docs/index.html                                    |   2 +-
 docs/news/index.html                               |   2 +-
 docs/reference/D24_2014-1.png                      | Bin 0 -> 88185 bytes
 docs/reference/D24_2014.html                       |  27 ++++++++-
 docs/reference/index.html                          |   2 +-
 docs/reference/mkinds.html                         |  12 ++--
 docs/reference/mkindsg.html                        |  12 +++-
 docs/reference/saem.html                           |  39 ++++---------
 docs/search.json                                   |   2 +-
 13 files changed, 91 insertions(+), 82 deletions(-)
 create mode 100644 docs/reference/D24_2014-1.png

(limited to 'docs')

diff --git a/docs/404.html b/docs/404.html
index 4a18a694..ce758d30 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/index.html b/docs/articles/index.html
index b9afdae8..69d79223 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/web_only/mesotrione_parent_2023.html b/docs/articles/web_only/mesotrione_parent_2023.html
index c9409d05..2fda7b31 100644
--- a/docs/articles/web_only/mesotrione_parent_2023.html
+++ b/docs/articles/web_only/mesotrione_parent_2023.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../../index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,7 +84,7 @@
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 12 September 2025
-(rebuilt 2025-09-12)</h4>
+(rebuilt 2025-11-28)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/mesotrione_parent_2023.rmd" class="external-link"><code>vignettes/web_only/mesotrione_parent_2023.rmd</code></a></small>
       <div class="d-none name"><code>mesotrione_parent_2023.rmd</code></div>
@@ -103,7 +103,7 @@ parameters. Because in some other case studies, the SFORB
 parameterisation of biexponential decline has shown some advantages over
 the DFOP parameterisation, SFORB was included in the list of tested
 models as well.</p>
-<p>The mkin package is used in version 1.2.11, which is contains the
+<p>The mkin package is used in version 1.2.10, which is contains the
 functions that were used for the evaluations. The <code>saemix</code>
 package is used as a backend for fitting the NLHM, but is also loaded to
 make the convergence plot function available.</p>
@@ -2429,7 +2429,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:01 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - k_meso * meso
@@ -2506,7 +2506,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:02 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
@@ -2589,7 +2589,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:02 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2680,7 +2680,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:02 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
@@ -2787,7 +2787,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:03 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - ifelse(time &lt;= tb, k1, k2) * meso
@@ -2880,7 +2880,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:15 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - k_meso * meso
@@ -2961,7 +2961,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:18 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
@@ -3048,7 +3048,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:21 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
@@ -3133,7 +3133,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:25 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3228,7 +3228,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:30 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3321,7 +3321,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:37 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3412,7 +3412,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:41 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
@@ -3523,7 +3523,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:44 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:24 2025 
 
 Equations:
 d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
@@ -3630,7 +3630,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:48 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:25 2025 
 
 Equations:
 d_meso/dt = - ifelse(time &lt;= tb, k1, k2) * meso
@@ -3727,7 +3727,7 @@ saemix version used for fitting:      3.4
 mkin version used for pre-fitting:  1.2.11 
 R version used for fitting:         4.5.1 
 Date of fit:     Fri Sep 12 22:15:50 2025 
-Date of summary: Fri Sep 12 22:15:50 2025 
+Date of summary: Fri Nov 28 09:15:25 2025 
 
 Equations:
 d_meso/dt = - ifelse(time &lt;= tb, k1, k2) * meso
@@ -3819,7 +3819,7 @@ meso 14.69 82.45    24.82   14.69   29.29
 <div class="section level3">
 <h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
 </h3>
-<pre><code>R version 4.5.1 (2025-06-13)
+<pre><code>R version 4.5.2 (2025-10-31)
 Platform: x86_64-pc-linux-gnu
 Running under: Debian GNU/Linux 13 (trixie)
 
@@ -3843,23 +3843,22 @@ attached base packages:
 [8] base     
 
 other attached packages:
-[1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.4      npde_3.5       
-[5] knitr_1.50      mkin_1.2.11    
+[1] saemix_3.4  npde_3.5    knitr_1.50  mkin_1.2.10
 
 loaded via a namespace (and not attached):
- [1] gtable_0.3.6       jsonlite_2.0.0     dplyr_1.1.4        compiler_4.5.1    
+ [1] gtable_0.3.6       jsonlite_2.0.0     dplyr_1.1.4        compiler_4.5.2    
  [5] tidyselect_1.2.1   dichromat_2.0-0.1  gridExtra_2.3      jquerylib_0.1.4   
- [9] systemfonts_1.2.3  scales_1.4.0       textshaping_1.0.1  readxl_1.4.5      
-[13] yaml_2.3.10        fastmap_1.2.0      lattice_0.22-7     ggplot2_3.5.2     
-[17] R6_2.6.1           generics_0.1.4     lmtest_0.9-40      MASS_7.3-65       
-[21] htmlwidgets_1.6.4  tibble_3.3.0       desc_1.4.3         bslib_0.9.0       
-[25] pillar_1.10.2      RColorBrewer_1.1-3 rlang_1.1.6        cachem_1.1.0      
-[29] xfun_0.52          fs_1.6.6           sass_0.4.10        cli_3.6.5         
-[33] pkgdown_2.1.3      magrittr_2.0.3     digest_0.6.37      grid_4.5.1        
-[37] mclust_6.1.1       lifecycle_1.0.4    nlme_3.1-168       vctrs_0.6.5       
-[41] evaluate_1.0.3     glue_1.8.0         cellranger_1.1.0   farver_2.1.2      
-[45] codetools_0.2-20   ragg_1.4.0         zoo_1.8-14         colorspace_2.1-1  
-[49] tools_4.5.1        pkgconfig_2.0.3    htmltools_0.5.8.1 </code></pre>
+ [9] systemfonts_1.2.3  scales_1.4.0       textshaping_1.0.1  yaml_2.3.10       
+[13] fastmap_1.2.0      lattice_0.22-7     ggplot2_3.5.2      R6_2.6.1          
+[17] generics_0.1.4     lmtest_0.9-40      MASS_7.3-65        htmlwidgets_1.6.4 
+[21] tibble_3.3.0       desc_1.4.3         bslib_0.9.0        pillar_1.10.2     
+[25] RColorBrewer_1.1-3 rlang_1.1.6        cachem_1.1.0       xfun_0.52         
+[29] fs_1.6.6           sass_0.4.10        cli_3.6.5          pkgdown_2.1.3     
+[33] magrittr_2.0.3     digest_0.6.37      grid_4.5.2         mclust_6.1.1      
+[37] lifecycle_1.0.4    nlme_3.1-168       vctrs_0.6.5        evaluate_1.0.3    
+[41] glue_1.8.0         farver_2.1.2       ragg_1.4.0         zoo_1.8-14        
+[45] colorspace_2.1-1   rmarkdown_2.29     tools_4.5.2        pkgconfig_2.0.3   
+[49] htmltools_0.5.8.1 </code></pre>
 </div>
 <div class="section level3">
 <h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
diff --git a/docs/authors.html b/docs/authors.html
index 9ac0a380..9529bffa 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -85,13 +85,13 @@
 
       <p>Ranke J (2025).
 <em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>.
-R package version 1.2.11, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>.
+R package version 1.2.10, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>.
 </p>
       <pre>@Manual{,
   title = {mkin: Kinetic Evaluation of Chemical Degradation Data},
   author = {Johannes Ranke},
   year = {2025},
-  note = {R package version 1.2.11},
+  note = {R package version 1.2.10},
   url = {https://pkgdown.jrwb.de/mkin/},
 }</pre>
     </div>
diff --git a/docs/index.html b/docs/index.html
index a204d9a4..3af372bd 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -22,7 +22,7 @@
 
     <a class="navbar-brand me-2" href="index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/news/index.html b/docs/news/index.html
index 0e096262..3014b19b 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/D24_2014-1.png b/docs/reference/D24_2014-1.png
new file mode 100644
index 00000000..3f04f216
Binary files /dev/null and b/docs/reference/D24_2014-1.png differ
diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html
index 2eee55fa..fed830e6 100644
--- a/docs/reference/D24_2014.html
+++ b/docs/reference/D24_2014.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014"><meta name="description" content="The five datasets were extracted from the active substance evaluation dossier
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en-GB"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014"><meta name="description" content="The five datasets were extracted from the active substance evaluation dossier
 published by EFSA. Kinetic evaluations shown for these datasets are intended
 to illustrate and advance kinetic modelling. The fact that these data and
 some results are shown here does not imply a license to use them in the
@@ -38,7 +38,7 @@ constrained by data protection regulations."></head><body>
     <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
-    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/mesotrione_parent_2023.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
     <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
     <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
@@ -193,6 +193,27 @@ specific pieces of information in the comments.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            * time))) * D24 - k_DCP * DCP</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA</span>
+<span class="r-in"><span><span class="va">D24_2014_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">D24_2014_data</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">f_D24_2014</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>  models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="st">"SFO-SFO-SFO"</span> <span class="op">=</span> <span class="va">m_D24</span>, </span></span>
+<span class="r-in"><span>    <span class="st">"DFOP-SFO-SFO"</span> <span class="op">=</span> <span class="va">m_D24_2</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  data <span class="op">=</span> <span class="va">D24_2014_data</span>,</span></span>
+<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_D24_2014</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               dataset</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> model          Mississippi Fayette RefSol 03-G Site E1 Site I2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO-SFO-SFO  OK          Z       Z           Z       Z      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   DFOP-SFO-SFO OK          Z       Z           Z       Z      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Z: Observations with value of zero were removed from the data</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_D24_2014</span><span class="op">[</span>, <span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="D24_2014-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
@@ -205,7 +226,7 @@ specific pieces of information in the comments.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 57af6079..ab9bbb13 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html
index 3c905b02..c45a1b0b 100644
--- a/docs/reference/mkinds.html
+++ b/docs/reference/mkinds.html
@@ -1,10 +1,10 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A dataset class for mkin — mkinds • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A dataset class for mkin — mkinds"><meta name="description" content="At the moment this dataset class is hardly used in mkin. For example,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en-GB"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A dataset class for mkin — mkinds • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A dataset class for mkin — mkinds"><meta name="description" content="At the moment this dataset class is hardly used in mkin. For example,
 mkinfit does not take mkinds datasets as argument, but works with dataframes
-such as the on contained in the data field of mkinds objects. Some datasets
+such as the one contained in the data field of mkinds objects. Some datasets
 provided by this package come as mkinds objects nevertheless."><meta property="og:description" content="At the moment this dataset class is hardly used in mkin. For example,
 mkinfit does not take mkinds datasets as argument, but works with dataframes
-such as the on contained in the data field of mkinds objects. Some datasets
+such as the one contained in the data field of mkinds objects. Some datasets
 provided by this package come as mkinds objects nevertheless."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
@@ -34,7 +34,7 @@ provided by this package come as mkinds objects nevertheless."></head><body>
     <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
-    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/mesotrione_parent_2023.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
     <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
     <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
@@ -69,7 +69,7 @@ provided by this package come as mkinds objects nevertheless."></head><body>
     <div class="ref-description section level2">
     <p>At the moment this dataset class is hardly used in mkin. For example,
 mkinfit does not take mkinds datasets as argument, but works with dataframes
-such as the on contained in the data field of mkinds objects. Some datasets
+such as the one contained in the data field of mkinds objects. Some datasets
 provided by this package come as mkinds objects nevertheless.</p>
     </div>
 
@@ -206,7 +206,7 @@ and value in order to be compatible with mkinfit</p></dd>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html
index 92d5cd12..6ab6f2f1 100644
--- a/docs/reference/mkindsg.html
+++ b/docs/reference/mkindsg.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A class for dataset groups for mkin — mkindsg • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A class for dataset groups for mkin — mkindsg"><meta name="description" content="A container for working with datasets that share at least one compound,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en-GB"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A class for dataset groups for mkin — mkindsg • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A class for dataset groups for mkin — mkindsg"><meta name="description" content="A container for working with datasets that share at least one compound,
 so that combined evaluations are desirable.
 Time normalisation factors are initialised with a value of 1 for each
 dataset if no data are supplied."><meta property="og:description" content="A container for working with datasets that share at least one compound,
@@ -34,7 +34,7 @@ dataset if no data are supplied."></head><body>
     <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
-    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/mesotrione_parent_2023.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
     <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
     <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
     <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
@@ -99,6 +99,12 @@ dataset if no data are supplied.</p>
 <dd><p>Not used.</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>Currently, no functions making use of the defined class structure
+are available in this package. Refer to <a href="D24_2014.html">D24_2014</a> for an example
+dataset in this structure, with some example evaluations.</p>
+    </div>
     <div class="section level2">
     <h2 id="public-fields">Public fields<a class="anchor" aria-label="anchor" href="#public-fields"></a></h2>
     <p></p><div class="r6-fields"><dl><dt><code>title</code></dt>
@@ -394,7 +400,7 @@ or covariates like soil pH).</p></dd>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/saem.html b/docs/reference/saem.html
index 159bc00f..4e81388b 100644
--- a/docs/reference/saem.html
+++ b/docs/reference/saem.html
@@ -11,7 +11,7 @@ Expectation Maximisation algorithm (SAEM)."></head><body>
 
     <a class="navbar-brand me-2" href="../index.html">mkin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -402,10 +402,10 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.4 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.11 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.5.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Fri Sep 12 20:34:55 2025 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Fri Sep 12 20:34:55 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.10 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.5.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Fri Nov 28 09:18:42 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Fri Nov 28 09:18:42 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
@@ -420,7 +420,7 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 4.017 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 3.833 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 10 chains</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Constant variance </span>
@@ -682,28 +682,11 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The following takes about 6 minutes</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_dfop_sfo_deSolve</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span></span>
-<span class="r-in"><span>  nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">200</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DINTDY-  T (=R1) illegal      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 70</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 53.1042, R2 = 56.6326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DINTDY-  T (=R1) illegal      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 53.1042, R2 = 56.6326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DLSODA-  Trouble in DINTDY.  ITASK = I1, TOUT = R1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, I1 = 1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error in deSolve::lsoda(y = odeini, times = outtimes_deSolve, func = lsoda_func,  : </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   illegal input detected before taking any integration steps - see written message</span>
+<span class="r-in"><span><span class="co"># The following took about 6 minutes and currently fails with</span></span></span>
+<span class="r-in"><span><span class="co"># "Error in deSolve::lsoda(y = odeini, times = outtimes_deSolve, func = lsoda_func,  : </span></span></span>
+<span class="r-in"><span><span class="co"># illegal input detected before taking any integration steps - see written message"</span></span></span>
+<span class="r-in"><span><span class="co">#f_saem_dfop_sfo_deSolve &lt;- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</span></span></span>
+<span class="r-in"><span><span class="co">#  nbiter.saemix = c(200, 80))</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#anova(</span></span></span>
 <span class="r-in"><span><span class="co">#  f_saem_dfop_sfo,</span></span></span>
diff --git a/docs/search.json b/docs/search.json
index fa330aa5..79534c2e 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l1","dir":"Articles","previous_headings":"","what":"Laboratory Data L1","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L1 FOCUS kinetics report, p. 284: use assumptions simple first order (SFO), case declining rate constant time (FOMC) case two different phases kinetics (DFOP). detailed discussion models, please see FOCUS kinetics report. Since mkin version 0.9-32 (July 2014), can use shorthand notation like \"SFO\" parent degradation models. following two lines fit model produce summary report model fit. covers numerical analysis given FOCUS report. plot fit obtained plot function mkinfit objects.  residual plot can easily obtained  comparison, FOMC model fitted well, χ2\\chi^2 error level checked.  get warning default optimisation algorithm Port converge, indication model overparameterised, .e. contains many parameters ill-defined consequence. fact, due higher number parameters, lower number degrees freedom fit, χ2\\chi^2 error level actually higher FOMC model (3.6%) SFO model (3.4%). Additionally, parameters log_alpha log_beta internally fitted model excessive confidence intervals, span 25 orders magnitude (!) backtransformed scale alpha beta. Also, t-test significant difference zero indicate significant difference, p-values greater 0.1, finally, parameter correlation log_alpha log_beta 1.000, clearly indicating model overparameterised. χ2\\chi^2 error levels reported Appendix 3 Appendix 7 FOCUS kinetics report rounded integer percentages partly deviate one percentage point results calculated mkin. reason known. However, mkin gives χ2\\chi^2 error levels kinfit package calculation routines kinfit package extensively compared results obtained KinGUI software, documented kinfit package vignette. KinGUI first widely used standard package field. Also, calculation χ2\\chi^2 error levels compared KinGUII, CAKE DegKin manager project sponsored German Umweltbundesamt (Ranke 2014).","code":"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) m.L1.SFO <- mkinfit(\"SFO\", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:26 2025  ## Date of summary: Mon May 12 21:54:26 2025  ##  ## Equations: ## d_parent/dt = - k_parent * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 133 model solutions performed in 0.011 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  0.10 deparm ##  ## Starting values for the transformed parameters actually optimised: ##                  value lower upper ## parent_0     89.850000  -Inf   Inf ## log_k_parent -2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC     BIC    logLik ##   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   -2.347    0.03763 -2.428 -2.267 ## sigma           2.780    0.46330  1.792  3.767 ##  ## Parameter correlation: ##                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  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 ## All data   3.424       2  7 ## parent     3.424       2  7 ##  ## Estimated disappearance times: ##         DT50  DT90 ## parent 7.249 24.08 ##  ## Data: ##  time variable observed predicted residual ##     0   parent     88.3    92.471  -4.1710 ##     0   parent     91.4    92.471  -1.0710 ##     1   parent     85.6    84.039   1.5610 ##     1   parent     84.5    84.039   0.4610 ##     2   parent     78.9    76.376   2.5241 ##     2   parent     77.6    76.376   1.2241 ##     3   parent     72.0    69.412   2.5884 ##     3   parent     71.9    69.412   2.4884 ##     5   parent     50.3    57.330  -7.0301 ##     5   parent     59.4    57.330   2.0699 ##     7   parent     47.0    47.352  -0.3515 ##     7   parent     45.1    47.352  -2.2515 ##    14   parent     27.7    24.247   3.4528 ##    14   parent     27.3    24.247   3.0528 ##    21   parent     10.0    12.416  -2.4163 ##    21   parent     10.4    12.416  -2.0163 ##    30   parent      2.9     5.251  -2.3513 ##    30   parent      4.0     5.251  -1.2513 plot(m.L1.SFO, show_errmin = TRUE, main = \"FOCUS L1 - SFO\") mkinresplot(m.L1.SFO, ylab = \"Observed\", xlab = \"Time\") 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\") summary(m.L1.FOMC, data = FALSE) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:26 2025  ## Date of summary: Mon May 12 21:54:26 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 342 model solutions performed in 0.024 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 89.85  state ## alpha     1.00 deparm ## beta     10.00 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  89.850000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ##  ## Warning(s):  ## Optimisation did not converge: ## false convergence (8) ##  ## Results: ##  ##        AIC      BIC    logLik ##   95.88782 99.44931 -43.94391 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error  Lower  Upper ## parent_0     92.47     1.2820 89.720 95.220 ## log_alpha    13.20        NaN    NaN    NaN ## log_beta     15.54        NaN    NaN    NaN ## sigma         2.78     0.4607  1.792  3.768 ##  ## Parameter correlation: ##           parent_0 log_alpha log_beta    sigma ## parent_0  1.000000       NaN      NaN 0.000603 ## log_alpha      NaN         1      NaN      NaN ## log_beta       NaN       NaN        1      NaN ## sigma     0.000603       NaN      NaN 1.000000 ##  ## 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 9.247e+01      NA     NA 89.720 95.220 ## alpha    5.386e+05      NA     NA     NA     NA ## beta     5.633e+06      NA     NA     NA     NA ## sigma    2.780e+00      NA     NA  1.792  3.768 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   3.619       3  6 ## parent     3.619       3  6 ##  ## Estimated disappearance times: ##         DT50  DT90 DT50back ## parent 7.249 24.08    7.249"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l2","dir":"Articles","previous_headings":"","what":"Laboratory Data L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L2 FOCUS kinetics report, p. 287:","code":"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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"sfo-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"SFO fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":", SFO model fitted result plotted. residual plot can obtained simply adding argument show_residuals plot command.  χ2\\chi^2 error level 14% suggests model fit well. also obvious plots fit, included residual plot. FOCUS kinetics report, stated apparent systematic error observed residual plot measured DT90 (approximately day 5), underestimation beyond point. may add difficult judge random nature residuals just three samplings days 0, 1 3. Also, clear priori consistent underestimation approximate DT90 irrelevant. However, can rationalised fact FOCUS fate models generally implement SFO kinetics.","code":"m.L2.SFO <- mkinfit(\"SFO\", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,      main = \"FOCUS L2 - SFO\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"fomc-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"FOMC fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"comparison, FOMC model fitted well, χ2\\chi^2 error level checked.  error level χ2\\chi^2 test passes much lower case. Therefore, FOMC model provides better description data, less experimental error assumed order explain data.","code":"m.L2.FOMC <- mkinfit(\"FOMC\", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE,      main = \"FOCUS L2 - FOMC\") summary(m.L2.FOMC, data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:26 2025  ## Date of summary: Mon May 12 21:54:26 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 239 model solutions performed in 0.015 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 93.95  state ## alpha     1.00 deparm ## beta     10.00 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  93.950000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   61.78966 63.72928 -26.89483 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error    Lower   Upper ## parent_0   93.7700     1.6130 90.05000 97.4900 ## log_alpha   0.3180     0.1559 -0.04149  0.6776 ## log_beta    0.2102     0.2493 -0.36460  0.7850 ## sigma       2.2760     0.4645  1.20500  3.3470 ##  ## Parameter correlation: ##             parent_0  log_alpha   log_beta      sigma ## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 ## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07 ## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.386e-07 ## sigma     -7.436e-09 -1.617e-07 -1.386e-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   93.770  58.120 4.267e-12 90.0500 97.490 ## alpha       1.374   6.414 1.030e-04  0.9594  1.969 ## beta        1.234   4.012 1.942e-03  0.6945  2.192 ## sigma       2.276   4.899 5.977e-04  1.2050  3.347 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   6.205       3  3 ## parent     6.205       3  3 ##  ## Estimated disappearance times: ##          DT50  DT90 DT50back ## parent 0.8092 5.356    1.612"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"dfop-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"DFOP fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"Fitting four parameter DFOP model reduces χ2\\chi^2 error level.  , DFOP model clearly best-fit model dataset L2 based chi^2 error level criterion.","code":"m.L2.DFOP <- mkinfit(\"DFOP\", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,      main = \"FOCUS L2 - DFOP\") summary(m.L2.DFOP, data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:27 2025  ##  ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * ##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) ##            * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 581 model solutions performed in 0.043 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 93.95  state ## k1        0.10 deparm ## k2        0.01 deparm ## g         0.50 deparm ##  ## Starting values for the transformed parameters actually optimised: ##              value lower upper ## parent_0 93.950000  -Inf   Inf ## log_k1   -2.302585  -Inf   Inf ## log_k2   -4.605170  -Inf   Inf ## g_qlogis  0.000000  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   52.36695 54.79148 -21.18347 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##          Estimate Std. Error      Lower     Upper ## parent_0   93.950  9.998e-01    91.5900   96.3100 ## log_k1      3.113  1.849e+03 -4369.0000 4375.0000 ## log_k2     -1.088  6.285e-02    -1.2370   -0.9394 ## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638 ## sigma       1.414  2.886e-01     0.7314    2.0960 ##  ## Parameter correlation: ##            parent_0     log_k1     log_k2   g_qlogis      sigma ## parent_0  1.000e+00  6.763e-07 -8.944e-10  2.665e-01 -1.083e-09 ## log_k1    6.763e-07  1.000e+00  1.112e-04 -2.187e-04 -1.027e-05 ## log_k2   -8.944e-10  1.112e-04  1.000e+00 -7.903e-01  9.464e-09 ## g_qlogis  2.665e-01 -2.187e-04 -7.903e-01  1.000e+00 -1.532e-08 ## sigma    -1.083e-09 -1.027e-05  9.464e-09 -1.532e-08  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  93.9500 9.397e+01 2.036e-12 91.5900 96.3100 ## k1        22.4900 5.533e-04 4.998e-01  0.0000     Inf ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data    2.53       4  2 ## parent      2.53       4  2 ##  ## Estimated disappearance times: ##          DT50  DT90 DT50back DT50_k1 DT50_k2 ## parent 0.5335 5.311    1.599 0.03083   2.058"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l3","dir":"Articles","previous_headings":"","what":"Laboratory Data L3","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L3 FOCUS kinetics report, p. 290.","code":"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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"fit-multiple-models","dir":"Articles","previous_headings":"Laboratory Data L3","what":"Fit multiple models","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"mkin version 0.9-39 (June 2015), can fit several models one datasets one call function mmkin. datasets passed list, case named list holding L3 dataset prepared .  χ2\\chi^2 error level 21% well plot suggest SFO model fit well. FOMC model performs better, error level χ2\\chi^2 test passes 7%. Fitting four parameter DFOP model reduces χ2\\chi^2 error level considerably.","code":"# 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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"accessing-mmkin-objects","dir":"Articles","previous_headings":"Laboratory Data L3","what":"Accessing mmkin objects","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"objects returned mmkin arranged like matrix, models row index datasets column index. can extract summary plot e.g. DFOP fit, using square brackets indexing result use summary plot functions working mkinfit objects.  , look model plot, confidence intervals parameters correlation matrix suggest parameter estimates reliable, DFOP model can used best-fit model based χ2\\chi^2 error level criterion laboratory data L3. also example standard t-test parameter g_ilr misleading, tests significant difference zero. case, zero appears correct value parameter, confidence interval backtransformed parameter g quite narrow.","code":"summary(mm.L3[[\"DFOP\", 1]]) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:27 2025  ##  ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * ##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) ##            * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 376 model solutions performed in 0.024 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 97.80  state ## k1        0.10 deparm ## k2        0.01 deparm ## g         0.50 deparm ##  ## Starting values for the transformed parameters actually optimised: ##              value lower upper ## parent_0 97.800000  -Inf   Inf ## log_k1   -2.302585  -Inf   Inf ## log_k2   -4.605170  -Inf   Inf ## g_qlogis  0.000000  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   32.97732 33.37453 -11.48866 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##          Estimate Std. Error   Lower      Upper ## parent_0  97.7500    1.01900 94.5000 101.000000 ## log_k1    -0.6612    0.10050 -0.9812  -0.341300 ## log_k2    -4.2860    0.04322 -4.4230  -4.148000 ## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142 ## sigma      1.0170    0.25430  0.2079   1.827000 ##  ## Parameter correlation: ##            parent_0     log_k1     log_k2   g_qlogis      sigma ## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.696e-08 ## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07 ## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06 ## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.930e-07 ## sigma    -9.696e-08  7.148e-07  1.022e-06 -7.930e-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 97.75000  95.960 1.248e-06 94.50000 101.00000 ## k1        0.51620   9.947 1.081e-03  0.37490   0.71090 ## k2        0.01376  23.140 8.840e-05  0.01199   0.01579 ## g         0.45660  34.920 2.581e-05  0.41540   0.49850 ## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   2.225       4  4 ## parent     2.225       4  4 ##  ## Estimated disappearance times: ##         DT50 DT90 DT50back DT50_k1 DT50_k2 ## parent 7.464  123    37.03   1.343   50.37 ##  ## Data: ##  time variable observed predicted residual ##     0   parent     97.8     97.75  0.05396 ##     3   parent     60.0     60.45 -0.44933 ##     7   parent     51.0     49.44  1.56338 ##    14   parent     43.0     43.84 -0.83632 ##    30   parent     35.0     35.15 -0.14707 ##    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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l4","dir":"Articles","previous_headings":"","what":"Laboratory Data L4","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L4 FOCUS kinetics report, p. 293: Fits SFO FOMC models, plots summaries produced :  χ2\\chi^2 error level 3.3% well plot suggest SFO model fits well. error level χ2\\chi^2 test passes slightly lower FOMC model. However, difference appears negligible.","code":"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) # 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),                quiet = TRUE) plot(mm.L4) summary(mm.L4[[\"SFO\", 1]], data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:28 2025  ##  ## Equations: ## d_parent/dt = - k_parent * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 142 model solutions performed in 0.01 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   0.1 deparm ##  ## Starting values for the transformed parameters actually optimised: ##                  value lower upper ## parent_0     96.600000  -Inf   Inf ## log_k_parent -2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   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   -5.030    0.07059 -5.211  -4.848 ## sigma           3.162    0.79050  1.130   5.194 ##  ## Parameter correlation: ##               parent_0 log_k_parent     sigma ## parent_0     1.000e+00    5.938e-01 3.430e-07 ## log_k_parent 5.938e-01    1.000e+00 5.885e-07 ## sigma        3.430e-07    5.885e-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  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 ## All data   3.287       2  6 ## parent     3.287       2  6 ##  ## Estimated disappearance times: ##        DT50 DT90 ## parent  106  352 summary(mm.L4[[\"FOMC\", 1]], data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:28 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 224 model solutions performed in 0.014 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0  96.6  state ## alpha      1.0 deparm ## beta      10.0 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  96.600000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   40.37255 40.69032 -16.18628 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error   Lower    Upper ## parent_0   99.1400     1.2670 95.6300 102.7000 ## log_alpha  -0.3506     0.2616 -1.0770   0.3756 ## log_beta    4.1740     0.3938  3.0810   5.2670 ## sigma       1.8300     0.4575  0.5598   3.1000 ##  ## Parameter correlation: ##             parent_0  log_alpha   log_beta      sigma ## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.447e-07 ## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.198e-08 ## log_beta  -5.543e-01  9.889e-01  1.000e+00  4.923e-08 ## sigma     -2.447e-07  2.198e-08  4.923e-08  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  99.1400  78.250 7.993e-08 95.6300 102.700 ## alpha      0.7042   3.823 9.365e-03  0.3407   1.456 ## beta      64.9800   2.540 3.201e-02 21.7800 193.900 ## sigma      1.8300   4.000 8.065e-03  0.5598   3.100 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   2.029       3  5 ## parent     2.029       3  5 ##  ## Estimated disappearance times: ##         DT50 DT90 DT50back ## parent 108.9 1644    494.9"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"abstract","dir":"Articles","previous_headings":"","what":"Abstract","title":"Short introduction to mkin","text":"regulatory evaluation chemical substances like plant protection products (pesticides), biocides chemicals, degradation data play important role. evaluation pesticide degradation experiments, detailed guidance developed, based nonlinear optimisation. R add-package mkin implements fitting models recommended guidance within R calculates statistical measures data series within one compartments, parent metabolites.","code":"library(\"mkin\", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                          M1 = mkinsub(\"SFO\", \"M2\"),                          M2 = mkinsub(\"SFO\"),                          use_of_ff = \"max\", quiet = TRUE)   # Produce model predictions using some arbitrary parameters 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),   sampling_times)  # Generate a dataset by adding normally distributed errors with # standard deviation 3, for two replicates at each sampling time d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2,                              sdfunc = function(x) 3,                              n = 1, seed = 123456789 )  # Fit the model to the dataset 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\"))"},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"Short introduction to mkin","text":"mkin package (J. Ranke 2021) implements approach degradation kinetics recommended kinetics report provided FOrum Co-ordination pesticide fate models USe (FOCUS Work Group Degradation Kinetics 2006, 2014). covers data series describing decline one compound, data series transformation products (commonly termed metabolites) data series one compartment. possible include back reactions. Therefore, equilibrium reactions equilibrium partitioning can specified, although often leads overparameterisation model. first mkin code published 2010, commonly used tools fitting complex kinetic degradation models experimental data KinGUI (Schäfer et al. 2007), MATLAB based tool graphical user interface specifically tailored task included output proposed FOCUS Kinetics Workgroup, ModelMaker, general purpose compartment based tool providing infrastructure fitting dynamic simulation models based differential equations data. ‘mkin’ code first uploaded BerliOS development platform. taken , version control history imported R-Forge site (see e.g. initial commit 11 May 2010), code still updated. time, R package FME (Flexible Modelling Environment) (Soetaert Petzoldt 2010) already available, provided good basis developing package specifically tailored task. remaining challenge make easy possible users (including author vignette) specify system differential equations include output requested FOCUS guidance, χ2\\chi^2 error level defined guidance. Also, mkin introduced using analytical solutions parent kinetics improved optimization speed. Later, Eigenvalue based solutions introduced mkin case linear differential equations (.e. FOMC DFOP models used parent compound), greatly improving optimization speed cases. , become somehow obsolete, use compiled code described gives even faster execution times. possibility specify back-reactions biphasic model (SFORB) metabolites present mkin beginning.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"derived-software-tools","dir":"Articles","previous_headings":"Background","what":"Derived software tools","title":"Short introduction to mkin","text":"Soon publication mkin, two derived tools published, namely KinGUII (developed Bayer Crop Science) CAKE (commissioned Tessella Syngenta), added graphical user interface (GUI), added fitting iteratively reweighted least squares (IRLS) characterisation likely parameter distributions Markov Chain Monte Carlo (MCMC) sampling. CAKE focuses smooth use experience, sacrificing flexibility model definition, originally allowing two primary metabolites parallel. current version 3.4 CAKE released May 2020 uses scheme six metabolites flexible arrangement supports biphasic modelling metabolites, support back-reactions (non-instantaneous equilibria). KinGUI offers even flexible widget specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) supported early , 2014, simple first-order models specified transformation products. Starting KinGUII version 2.1, biphasic modelling metabolites also available KinGUII. graphical user interface (GUI) recently brought decent degree maturity browser based GUI named gmkin. Please see documentation page manual information. comparison scope, usability numerical results obtained tools recently published Johannes Ranke, Wöltjen, Meinecke (2018).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"unique-features","dir":"Articles","previous_headings":"","what":"Unique features","title":"Short introduction to mkin","text":"Currently, main unique features available mkin speed increase using compiled code compiler present, parallel model fitting multicore machines using mmkin function, estimation parameter confidence intervals based transformed parameters (see ) possibility use two-component error model iteratively reweighted least squares fitting different variances variable introduced Gao et al. (2011) available mkin since version 0.9-22. release 0.9.49.5, IRLS algorithm complemented direct step-wise maximisation likelihood function, makes possible fit variance variable error model also two-component error model inspired error models developed analytical chemistry (Johannes Ranke Meinecke 2019).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"internal-parameter-transformations","dir":"Articles","previous_headings":"","what":"Internal parameter transformations","title":"Short introduction to mkin","text":"rate constants, log transformation used, proposed Bates Watts (1988, 77, 149). Approximate intervals constructed transformed rate constants (compare Bates Watts 1988, 135), .e. logarithms. Confidence intervals rate constants obtained using appropriate backtransformation using exponential function. first version mkin allowing specifying models using formation fractions, home-made reparameterisation used order ensure sum formation fractions exceed unity. method still used current version KinGUII (v2.1 April 2014), modification allows fixing pathway sink zero. CAKE uses penalties objective function order enforce constraint. 2012, alternative reparameterisation formation fractions proposed together René Lehmann (J. Ranke Lehmann 2012), based isometric logratio transformation (ILR). aim improve validity linear approximation objective function parameter estimation procedure well subsequent calculation parameter confidence intervals. current version mkin, logit transformation used parameters bound 0 1, g parameter DFOP model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"confidence-intervals-based-on-transformed-parameters","dir":"Articles","previous_headings":"Internal parameter transformations","what":"Confidence intervals based on transformed parameters","title":"Short introduction to mkin","text":"first attempt providing improved parameter confidence intervals introduced mkin 2013, confidence intervals obtained FME transformed parameters simply backtransformed one one yield asymmetric confidence intervals backtransformed parameters. However, 1:1 relation rate constants model transformed parameters fitted model, parameters obtained isometric logratio transformation calculated set formation fractions quantify paths compounds formed specific parent compound, 1:1 relation exists. Therefore, parameter confidence intervals formation fractions obtained method appear valid case single transformation product, currently logit transformation used formation fraction. confidence intervals obtained backtransformation cases 1:1 relation transformed original parameter exist considered author vignette accurate obtained using re-estimation Hessian matrix backtransformation, implemented FME package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"parameter-t-test-based-on-untransformed-parameters","dir":"Articles","previous_headings":"Internal parameter transformations","what":"Parameter t-test based on untransformed parameters","title":"Short introduction to mkin","text":"standard output many nonlinear regression software packages includes results test significant difference zero parameters. test also recommended check validity rate constants FOCUS guidance (FOCUS Work Group Degradation Kinetics 2014, 96ff). argued precondition test, .e. normal distribution estimator parameters, fulfilled case nonlinear regression (J. Ranke Lehmann 2015). However, test commonly used industry, consultants national authorities order decide reliability parameter estimates, based FOCUS guidance mentioned . Therefore, results one-sided t-test included summary output mkin. reasonable test significant difference transformed parameters (e.g. log(k)log(k)) zero, t-test calculated based model definition parameter transformation, .e. similar way packages apply internal parameter transformation. note included mkin output, pointing fact t-test based unjustified assumption normal distribution parameter estimators.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS, serial formation two metabolites can fitted mkin package. assembled course work package 1.2 Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) German Environment Agency carried 2022 2023. mkin package used version 1.2.10 currently development. newly introduced functionality used simplification excluding random effects set fits based related set fits reduced model, documentation starting parameters fit, starting parameters saem fits now listed summary. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores()  # We need to start a new cluster after defining a compiled model that is # saved as a DLL to the user directory, therefore we define a function # This is used again after defining the pathway model start_cluster <- function(n_cores) {   if (Sys.info()[\"sysname\"] == \"Windows\") {     ret <- makePSOCKcluster(n_cores)   } else {     ret <- makeForkCluster(n_cores)   }   return(ret) } cl <- start_cluster(n_cores)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"test-data","dir":"Articles > Prebuilt","previous_headings":"Introduction","what":"Test data","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"example data taken final addendum DAR 2014 distributed mkin package. Residue data time step normalisation factors read using function read_spreadsheet mkin package. function also performs time step normalisation. following tables show covariate data 5 datasets read spreadsheet file. Covariate data Dataset Nambsheim Dataset Tama Dataset Gross-Umstadt Dataset Sassafras Dataset Lleida","code":"data_file <- system.file(   \"testdata\", \"cyantraniliprole_soil_efsa_2014.xlsx\",   package = \"mkin\") cyan_ds <- read_spreadsheet(data_file, parent_only = FALSE) pH <- attr(cyan_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(cyan_ds)) {   print(     kable(mkin_long_to_wide(cyan_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE))     cat(\"\\n\\\\clearpage\\n\") }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"parent-only-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Parent only evaluations","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"pathway fits long run times, evaluations parent data performed first, order determine hierarchical parent degradation model random effects degradation model parameters ill-defined. fits converged successfully. almost models, random effect initial concentration parent compound ill-defined. biexponential models DFOP SFORB, random effect one additional parameter ill-defined two-component error model used. Model comparison based AIC BIC indicates two-component error model preferable parent models exception DFOP. lowest AIC BIC values obtained FOMC model, followed SFORB DFOP.","code":"cyan_sep_const <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\"),   cyan_ds, quiet = TRUE, cores = n_cores) cyan_sep_tc <- update(cyan_sep_const, error_model = \"tc\") cyan_saem_full <- mhmkin(list(cyan_sep_const, cyan_sep_tc)) status(cyan_saem_full) |> kable() illparms(cyan_saem_full) |> kable() anova(cyan_saem_full) |> kable(digits = 1) stopCluster(cl)"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"evaluations-with-pathway-established-previously","dir":"Articles > Prebuilt","previous_headings":"Pathway fits","what":"Evaluations with pathway established previously","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"test technical feasibility coupling relevant parent degradation models different transformation pathway models, list mkinmod models set . EU evaluation, parallel formation metabolites JCZ38 J9Z38 secondary formation metabolite JSE76 JCZ38 used. obtain suitable starting values NLHM fits, separate pathway fits performed datasets. separate fits converged successfully. biggest convergence problems seen using HS model constant variance. hierarchical pathway fits, random effects quantified corresponding parent data analyses excluded. code , output illparms function parent fits used argument no_random_effect mhmkin function. possibility introduced mkin version 1.2.2 currently development. status information individual fits shows fits completed successfully. matrix entries Fth FO indicate Fisher Information Matrix inverted fixed effects (theta) random effects (Omega), respectively. affected fits, ill-defined parameters determined using illparms function, relies Fisher Information Matrix. model comparisons suggest pathway fits using DFOP SFORB parent compound provide best fit. two parent model, successful fits shown . Plots fits parent models shown Appendix. DFOP pathway fit two-component error SFORB pathway fit two-component error closer graphical analysis Figures shows residues transformation product JCZ38 soils Tama Nambsheim observed later time points strongly systematically underestimated.","code":"if (!dir.exists(\"cyan_dlls\")) dir.create(\"cyan_dlls\") cyan_path_1 <- list(   sfo_path_1 = mkinmod(     cyan = mkinsub(\"SFO\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"sfo_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   fomc_path_1 = mkinmod(     cyan = mkinsub(\"FOMC\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"fomc_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   dfop_path_1 = mkinmod(     cyan = mkinsub(\"DFOP\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"dfop_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   sforb_path_1 = mkinmod(     cyan = mkinsub(\"SFORB\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"sforb_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   hs_path_1 = mkinmod(     cyan = mkinsub(\"HS\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"hs_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE) ) cl_path_1 <- start_cluster(n_cores) f_sep_1_const <- mmkin(   cyan_path_1,   cyan_ds,   error_model = \"const\",   cluster = cl_path_1,   quiet = TRUE) status(f_sep_1_const) |> kable() f_sep_1_tc <- update(f_sep_1_const, error_model = \"tc\") status(f_sep_1_tc) |> kable() f_saem_1 <- mhmkin(list(f_sep_1_const, f_sep_1_tc),   no_random_effect = illparms(cyan_saem_full),   cluster = cl_path_1) status(f_saem_1) |> kable() illparms(f_saem_1) |> kable() anova(f_saem_1[, \"const\"]) |> kable(digits = 1) anova(f_saem_1[1:4, ]) |> kable(digits = 1) plot(f_saem_1[[\"dfop_path_1\", \"tc\"]]) plot(f_saem_1[[\"sforb_path_1\", \"tc\"]]) stopCluster(cl_path_1)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"alternative-pathway-fits","dir":"Articles > Prebuilt","previous_headings":"Pathway fits","what":"Alternative pathway fits","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"improve fit JCZ38, back-reaction JSE76 JCZ38 introduced alternative version transformation pathway, analogy back-reaction K5A78 K5A77. pairs transformation products pairs organic acid corresponding amide (Addendum 2014, p. 109). FOMC provided best fit parent, biexponential models DFOP SFORB provided best initial pathway fits, three parent models used alternative pathway fits. Using constant variance, separate fits converge exception fits Sassafras soil data. Using two-component error model, separate fits converge exception alternative pathway fit DFOP used parent Sassafras dataset. hierarchical fits alternative pathway completed successfully, exception model using FOMC parent compound constant variance error model. biphasic fits (DFOP SFORB parent compound), random effects formation fractions pathways JCZ38 JSE76, reverse pathway JSE76 JCZ38 ill-defined. variants using biexponential models DFOP SFORB parent compound two-component error model give lowest AIC BIC values plotted . Compared original pathway, AIC BIC values indicate large improvement. confirmed plots, show metabolite JCZ38 fitted much better model. FOMC pathway fit two-component error, alternative pathway DFOP pathway fit two-component error, alternative pathway SFORB pathway fit two-component error, alternative pathway","code":"cyan_path_2 <- list(   fomc_path_2 = mkinmod(     cyan = mkinsub(\"FOMC\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\", \"JCZ38\"),     name = \"fomc_path_2\", quiet = TRUE,     dll_dir = \"cyan_dlls\",     overwrite = TRUE   ),   dfop_path_2 = mkinmod(     cyan = mkinsub(\"DFOP\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\", \"JCZ38\"),     name = \"dfop_path_2\", quiet = TRUE,     dll_dir = \"cyan_dlls\",     overwrite = TRUE   ),   sforb_path_2 = mkinmod(     cyan = mkinsub(\"SFORB\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\", \"JCZ38\"),     name = \"sforb_path_2\", quiet = TRUE,     dll_dir = \"cyan_dlls\",     overwrite = TRUE   ) )  cl_path_2 <- start_cluster(n_cores) f_sep_2_const <- mmkin(   cyan_path_2,   cyan_ds,   error_model = \"const\",   cluster = cl_path_2,   quiet = TRUE)  status(f_sep_2_const) |> kable() f_sep_2_tc <- update(f_sep_2_const, error_model = \"tc\") status(f_sep_2_tc) |> kable() f_saem_2 <- mhmkin(list(f_sep_2_const, f_sep_2_tc),   no_random_effect = illparms(cyan_saem_full[2:4, ]),   cluster = cl_path_2) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable() anova(f_saem_2[, \"tc\"]) |> kable(digits = 1) anova(f_saem_2[2:3,]) |> kable(digits = 1) plot(f_saem_2[[\"fomc_path_2\", \"tc\"]]) plot(f_saem_2[[\"dfop_path_2\", \"tc\"]]) plot(f_saem_2[[\"sforb_path_2\", \"tc\"]])"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"refinement-of-alternative-pathway-fits","dir":"Articles > Prebuilt","previous_headings":"Pathway fits","what":"Refinement of alternative pathway fits","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"ill-defined random effects identified parent fits pathway fits, excluded final evaluations . purpose, list character vectors created can indexed row column indices, contains degradation parameter names random effects excluded hierarchical fits contained f_saem_2. exception FOMC pathway fit constant variance, updated fits completed successfully. However, Fisher Information Matrix fixed effects (Fth) inverted, confidence intervals optimised parameters available. AIC BIC values best fit (DFOP pathway fit two-component error) lower previous fits alternative pathway, practical value refined evaluations limited confidence intervals obtained.","code":"no_ranef <- matrix(list(), nrow = 3, ncol = 2, dimnames = dimnames(f_saem_2)) no_ranef[[\"fomc_path_2\", \"const\"]] <- c(\"log_beta\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"fomc_path_2\", \"tc\"]] <- c(\"cyan_0\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"dfop_path_2\", \"const\"]] <- c(\"cyan_0\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"dfop_path_2\", \"tc\"]] <- c(\"cyan_0\", \"log_k1\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"sforb_path_2\", \"const\"]] <- c(\"cyan_free_0\",   \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"sforb_path_2\", \"tc\"]] <- c(\"cyan_free_0\", \"log_k_cyan_free_bound\",   \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") clusterExport(cl_path_2, \"no_ranef\")  f_saem_3 <- update(f_saem_2,   no_random_effect = no_ranef,   cluster = cl_path_2) status(f_saem_3) |> kable() illparms(f_saem_3) |> kable() anova(f_saem_3[, \"tc\"]) |> kable(digits = 1) anova(f_saem_3[2:3,]) |> kable(digits = 1) stopCluster(cl_path_2)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"conclusion","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusion","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"demonstrated relatively complex transformation pathway parallel formation two primary metabolites one secondary metabolite can fitted even data individual datasets quite different partly cover formation phase. run times pathway fits several hours, limiting practical feasibility iterative refinements based ill-defined parameters alternative checks parameter identifiability based multistart runs.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"acknowledgements","dir":"Articles > Prebuilt","previous_headings":"","what":"Acknowledgements","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"helpful comments Janina Wöltjen German Environment Agency gratefully acknowledged.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"plots-of-fits-that-were-not-refined-further","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Plots of fits that were not refined further","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"SFO pathway fit two-component error FOMC pathway fit two-component error HS pathway fit two-component error","code":"plot(f_saem_1[[\"sfo_path_1\", \"tc\"]]) plot(f_saem_1[[\"fomc_path_1\", \"tc\"]]) plot(f_saem_1[[\"sforb_path_1\", \"tc\"]])"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"pathway-1","dir":"Articles > Prebuilt","previous_headings":"Appendix > Hierarchical fit listings","what":"Pathway 1","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"Hierarchical SFO path 1 fit constant variance  Hierarchical SFO path 1 fit two-component error  Hierarchical FOMC path 1 fit constant variance  Hierarchical FOMC path 1 fit two-component error  Hierarchical DFOP path 1 fit constant variance  Hierarchical DFOP path 1 fit two-component error  Hierarchical SFORB path 1 fit constant variance  Hierarchical SFORB path 1 fit two-component error  Hierarchical HS path 1 fit constant variance","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:35:03 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - k_cyan * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 445.08 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0     log_k_cyan    log_k_JCZ38    log_k_J9Z38    log_k_JSE76         95.3304        -3.8459        -3.1305        -5.0678        -5.3196    f_cyan_ilr_1   f_cyan_ilr_2 f_JCZ38_qlogis          0.8158        23.5335        11.8774   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_0          4.797     0.0000       0.000       0.000      0.0000 log_k_cyan      0.000     0.9619       0.000       0.000      0.0000 log_k_JCZ38     0.000     0.0000       2.139       0.000      0.0000 log_k_J9Z38     0.000     0.0000       0.000       1.639      0.0000 log_k_JSE76     0.000     0.0000       0.000       0.000      0.7894 f_cyan_ilr_1    0.000     0.0000       0.000       0.000      0.0000 f_cyan_ilr_2    0.000     0.0000       0.000       0.000      0.0000 f_JCZ38_qlogis  0.000     0.0000       0.000       0.000      0.0000                f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_0               0.0000        0.000           0.00 log_k_cyan           0.0000        0.000           0.00 log_k_JCZ38          0.0000        0.000           0.00 log_k_J9Z38          0.0000        0.000           0.00 log_k_JSE76          0.0000        0.000           0.00 f_cyan_ilr_1         0.7714        0.000           0.00 f_cyan_ilr_2         0.0000        9.247           0.00 f_JCZ38_qlogis       0.0000        0.000          16.61  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2693 2687  -1331  Optimised parameters:                      est.      lower      upper cyan_0            95.1279  9.354e+01  9.671e+01 log_k_cyan        -3.8527 -4.367e+00 -3.338e+00 log_k_JCZ38       -3.0381 -4.187e+00 -1.889e+00 log_k_J9Z38       -5.0095 -5.623e+00 -4.396e+00 log_k_JSE76       -5.3357 -6.025e+00 -4.646e+00 f_cyan_ilr_1       0.8050  5.174e-01  1.093e+00 f_cyan_ilr_2      12.4820 -1.050e+06  1.051e+06 f_JCZ38_qlogis     1.2912  3.561e-01  2.226e+00 a.1                4.8393         NA         NA SD.log_k_cyan      0.5840         NA         NA SD.log_k_JCZ38     1.2740         NA         NA SD.log_k_J9Z38     0.3172         NA         NA SD.log_k_JSE76     0.5677         NA         NA SD.f_cyan_ilr_1    0.2623         NA         NA SD.f_cyan_ilr_2    1.3724         NA         NA SD.f_JCZ38_qlogis  0.1464         NA         NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_cyan     0.5840    NA    NA SD.log_k_JCZ38    1.2740    NA    NA SD.log_k_J9Z38    0.3172    NA    NA SD.log_k_JSE76    0.5677    NA    NA SD.f_cyan_ilr_1   0.2623    NA    NA SD.f_cyan_ilr_2   1.3724    NA    NA SD.f_JCZ38_qlogis 0.1464    NA    NA  Variance model:      est. lower upper a.1 4.839    NA    NA  Backtransformed parameters:                       est.     lower     upper cyan_0           95.127935 93.542456 96.713413 k_cyan            0.021221  0.012687  0.035497 k_JCZ38           0.047924  0.015189  0.151213 k_J9Z38           0.006674  0.003612  0.012332 k_JSE76           0.004817  0.002417  0.009601 f_cyan_to_JCZ38   0.757402        NA        NA f_cyan_to_J9Z38   0.242597        NA        NA f_JCZ38_to_JSE76  0.784347  0.588098  0.902582  Resulting formation fractions:                    ff cyan_JCZ38  7.574e-01 cyan_J9Z38  2.426e-01 cyan_sink   9.839e-08 JCZ38_JSE76 7.843e-01 JCZ38_sink  2.157e-01  Estimated disappearance times:         DT50   DT90 cyan   32.66 108.50 JCZ38  14.46  48.05 J9Z38 103.86 345.00 JSE76 143.91 478.04 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:34:47 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - k_cyan * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 428.46 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0     log_k_cyan    log_k_JCZ38    log_k_J9Z38    log_k_JSE76         96.0039        -3.8907        -3.1276        -5.0069        -4.9367    f_cyan_ilr_1   f_cyan_ilr_2 f_JCZ38_qlogis          0.7937        22.3422        17.8932   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_0          4.859      0.000        0.00        0.00      0.0000 log_k_cyan      0.000      0.962        0.00        0.00      0.0000 log_k_JCZ38     0.000      0.000        2.04        0.00      0.0000 log_k_J9Z38     0.000      0.000        0.00        1.72      0.0000 log_k_JSE76     0.000      0.000        0.00        0.00      0.9076 f_cyan_ilr_1    0.000      0.000        0.00        0.00      0.0000 f_cyan_ilr_2    0.000      0.000        0.00        0.00      0.0000 f_JCZ38_qlogis  0.000      0.000        0.00        0.00      0.0000                f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_0               0.0000        0.000           0.00 log_k_cyan           0.0000        0.000           0.00 log_k_JCZ38          0.0000        0.000           0.00 log_k_J9Z38          0.0000        0.000           0.00 log_k_JSE76          0.0000        0.000           0.00 f_cyan_ilr_1         0.7598        0.000           0.00 f_cyan_ilr_2         0.0000        8.939           0.00 f_JCZ38_qlogis       0.0000        0.000          14.49  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2658 2651  -1312  Optimised parameters:                       est. lower upper cyan_0            94.81681    NA    NA log_k_cyan        -3.91558    NA    NA log_k_JCZ38       -3.12715    NA    NA log_k_J9Z38       -5.04840    NA    NA log_k_JSE76       -5.10443    NA    NA f_cyan_ilr_1       0.80760    NA    NA f_cyan_ilr_2      48.66960    NA    NA f_JCZ38_qlogis     3.03397    NA    NA a.1                3.93879    NA    NA b.1                0.08057    NA    NA SD.log_k_cyan      0.58921    NA    NA SD.log_k_JCZ38     1.29813    NA    NA SD.log_k_J9Z38     0.68372    NA    NA SD.log_k_JSE76     0.35128    NA    NA SD.f_cyan_ilr_1    0.38352    NA    NA SD.f_cyan_ilr_2    4.98884    NA    NA SD.f_JCZ38_qlogis  1.75636    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_cyan     0.5892    NA    NA SD.log_k_JCZ38    1.2981    NA    NA SD.log_k_J9Z38    0.6837    NA    NA SD.log_k_JSE76    0.3513    NA    NA SD.f_cyan_ilr_1   0.3835    NA    NA SD.f_cyan_ilr_2   4.9888    NA    NA SD.f_JCZ38_qlogis 1.7564    NA    NA  Variance model:        est. lower upper a.1 3.93879    NA    NA b.1 0.08057    NA    NA  Backtransformed parameters:                      est. lower upper cyan_0           94.81681    NA    NA k_cyan            0.01993    NA    NA k_JCZ38           0.04384    NA    NA k_J9Z38           0.00642    NA    NA k_JSE76           0.00607    NA    NA f_cyan_to_JCZ38   0.75807    NA    NA f_cyan_to_J9Z38   0.24193    NA    NA f_JCZ38_to_JSE76  0.95409    NA    NA  Resulting formation fractions:                  ff cyan_JCZ38  0.75807 cyan_J9Z38  0.24193 cyan_sink   0.00000 JCZ38_JSE76 0.95409 JCZ38_sink  0.04591  Estimated disappearance times:         DT50   DT90 cyan   34.78 115.54 JCZ38  15.81  52.52 J9Z38 107.97 358.68 JSE76 114.20 379.35 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:35:21 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 462.739 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.2314        -3.3680        -5.1108        -5.9416         0.7144    f_cyan_ilr_2 f_JCZ38_qlogis      log_alpha       log_beta          7.0229        14.9234        -0.1791         2.9811   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.416       0.000         0.0       0.000       0.0000 log_k_JCZ38     0.000       2.439         0.0       0.000       0.0000 log_k_J9Z38     0.000       0.000         1.7       0.000       0.0000 log_k_JSE76     0.000       0.000         0.0       1.856       0.0000 f_cyan_ilr_1    0.000       0.000         0.0       0.000       0.7164 f_cyan_ilr_2    0.000       0.000         0.0       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000         0.0       0.000       0.0000 log_alpha       0.000       0.000         0.0       0.000       0.0000 log_beta        0.000       0.000         0.0       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta cyan_0                 0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          11.57           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          18.81    0.0000   0.0000 log_alpha              0.00           0.00    0.4144   0.0000 log_beta               0.00           0.00    0.0000   0.5077  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2428 2421  -1196  Optimised parameters:                       est.    lower    upper cyan_0            101.1664 98.51265 103.8202 log_k_JCZ38        -3.3883 -4.78250  -1.9941 log_k_J9Z38        -5.3087 -5.91564  -4.7017 log_k_JSE76        -6.1313 -7.30061  -4.9619 f_cyan_ilr_1        0.7456  0.43782   1.0534 f_cyan_ilr_2        0.8181  0.24956   1.3866 f_JCZ38_qlogis      2.0467  0.61165   3.4817 log_alpha          -0.2391 -0.62806   0.1499 log_beta            2.8739  2.67664   3.0711 a.1                 3.4160  3.17960   3.6525 SD.cyan_0           2.4355  0.40399   4.4671 SD.log_k_JCZ38      1.5654  0.57311   2.5576 SD.log_k_J9Z38      0.4645 -0.06533   0.9943 SD.log_k_JSE76      0.9841  0.10738   1.8609 SD.f_cyan_ilr_1     0.3285  0.10546   0.5515 SD.f_cyan_ilr_2     0.2276 -0.38711   0.8424 SD.f_JCZ38_qlogis   0.8340 -0.20970   1.8777 SD.log_alpha        0.4250  0.16017   0.6898  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_lph log_k_JCZ38    -0.0159                                                         log_k_J9Z38    -0.0546  0.0080                                                 log_k_JSE76    -0.0337  0.0016  0.0074                                         f_cyan_ilr_1   -0.0095  0.0194 -0.1573  0.0003                                 f_cyan_ilr_2   -0.2733  0.0799  0.3059  0.0263  0.0125                         f_JCZ38_qlogis  0.0755 -0.0783 -0.0516  0.1222 -0.1155 -0.5231                 log_alpha      -0.0567  0.0120  0.0351  0.0189  0.0040  0.0829 -0.0502         log_beta       -0.2980  0.0461  0.1382  0.0758  0.0209  0.4079 -0.2053  0.2759  Random effects:                     est.    lower  upper SD.cyan_0         2.4355  0.40399 4.4671 SD.log_k_JCZ38    1.5654  0.57311 2.5576 SD.log_k_J9Z38    0.4645 -0.06533 0.9943 SD.log_k_JSE76    0.9841  0.10738 1.8609 SD.f_cyan_ilr_1   0.3285  0.10546 0.5515 SD.f_cyan_ilr_2   0.2276 -0.38711 0.8424 SD.f_JCZ38_qlogis 0.8340 -0.20970 1.8777 SD.log_alpha      0.4250  0.16017 0.6898  Variance model:      est. lower upper a.1 3.416  3.18 3.652  Backtransformed parameters:                       est.     lower     upper cyan_0           1.012e+02 9.851e+01 103.82023 k_JCZ38          3.377e-02 8.375e-03   0.13614 k_J9Z38          4.948e-03 2.697e-03   0.00908 k_JSE76          2.174e-03 6.751e-04   0.00700 f_cyan_to_JCZ38  6.389e-01        NA        NA f_cyan_to_J9Z38  2.226e-01        NA        NA f_JCZ38_to_JSE76 8.856e-01 6.483e-01   0.97016 alpha            7.873e-01 5.336e-01   1.16166 beta             1.771e+01 1.454e+01  21.56509  Resulting formation fractions:                 ff cyan_JCZ38  0.6389 cyan_J9Z38  0.2226 cyan_sink   0.1385 JCZ38_JSE76 0.8856 JCZ38_sink  0.1144  Estimated disappearance times:         DT50    DT90 DT50back cyan   25.00  312.06    93.94 JCZ38  20.53   68.19       NA J9Z38 140.07  465.32       NA JSE76 318.86 1059.22       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:35:31 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 472.067 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1       101.13294       -3.32499       -5.09097       -5.93566        0.71359    f_cyan_ilr_2 f_JCZ38_qlogis      log_alpha       log_beta        10.30315       14.62272       -0.09633        3.10634   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.649       0.000        0.00        0.00       0.0000 log_k_JCZ38     0.000       2.319        0.00        0.00       0.0000 log_k_J9Z38     0.000       0.000        1.73        0.00       0.0000 log_k_JSE76     0.000       0.000        0.00        1.86       0.0000 f_cyan_ilr_1    0.000       0.000        0.00        0.00       0.7183 f_cyan_ilr_2    0.000       0.000        0.00        0.00       0.0000 f_JCZ38_qlogis  0.000       0.000        0.00        0.00       0.0000 log_alpha       0.000       0.000        0.00        0.00       0.0000 log_beta        0.000       0.000        0.00        0.00       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta cyan_0                 0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          12.85           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          18.54    0.0000   0.0000 log_alpha              0.00           0.00    0.3142   0.0000 log_beta               0.00           0.00    0.0000   0.7333  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2424 2416  -1193  Optimised parameters:                        est. lower upper cyan_0            100.65667    NA    NA log_k_JCZ38        -3.45782    NA    NA log_k_J9Z38        -5.23476    NA    NA log_k_JSE76        -5.71827    NA    NA f_cyan_ilr_1        0.68389    NA    NA f_cyan_ilr_2        0.61027    NA    NA f_JCZ38_qlogis    116.27482    NA    NA log_alpha          -0.14484    NA    NA log_beta            3.03220    NA    NA a.1                 3.11051    NA    NA b.1                 0.04508    NA    NA SD.log_k_JCZ38      1.39961    NA    NA SD.log_k_J9Z38      0.57920    NA    NA SD.log_k_JSE76      0.68364    NA    NA SD.f_cyan_ilr_1     0.31477    NA    NA SD.f_cyan_ilr_2     0.37716    NA    NA SD.f_JCZ38_qlogis   5.52695    NA    NA SD.log_alpha        0.22823    NA    NA SD.log_beta         0.39161    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.3996    NA    NA SD.log_k_J9Z38    0.5792    NA    NA SD.log_k_JSE76    0.6836    NA    NA SD.f_cyan_ilr_1   0.3148    NA    NA SD.f_cyan_ilr_2   0.3772    NA    NA SD.f_JCZ38_qlogis 5.5270    NA    NA SD.log_alpha      0.2282    NA    NA SD.log_beta       0.3916    NA    NA  Variance model:        est. lower upper a.1 3.11051    NA    NA b.1 0.04508    NA    NA  Backtransformed parameters:                       est. lower upper cyan_0           1.007e+02    NA    NA k_JCZ38          3.150e-02    NA    NA k_J9Z38          5.328e-03    NA    NA k_JSE76          3.285e-03    NA    NA f_cyan_to_JCZ38  5.980e-01    NA    NA f_cyan_to_J9Z38  2.273e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA alpha            8.652e-01    NA    NA beta             2.074e+01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.5980 cyan_J9Z38  0.2273 cyan_sink   0.1746 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000  Estimated disappearance times:         DT50  DT90 DT50back cyan   25.48 276.2    83.15 JCZ38  22.01  73.1       NA J9Z38 130.09 432.2       NA JSE76 210.98 700.9       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:36:02 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 503.671 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.0643        -3.4008        -5.0024        -5.8612         0.6855    f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2       g_qlogis          1.2366        13.6901        -1.8641        -4.5063        -0.6468   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          4.466       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       2.382       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.595       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       1.245       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6852 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 log_k1          0.000       0.000       0.000       0.000       0.0000 log_k2          0.000       0.000       0.000       0.000       0.0000 g_qlogis        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis cyan_0                 0.00           0.00 0.0000 0.0000    0.000 log_k_JCZ38            0.00           0.00 0.0000 0.0000    0.000 log_k_J9Z38            0.00           0.00 0.0000 0.0000    0.000 log_k_JSE76            0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_1           0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_2           1.28           0.00 0.0000 0.0000    0.000 f_JCZ38_qlogis         0.00          16.08 0.0000 0.0000    0.000 log_k1                 0.00           0.00 0.9866 0.0000    0.000 log_k2                 0.00           0.00 0.0000 0.5953    0.000 g_qlogis               0.00           0.00 0.0000 0.0000    1.583  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2403 2395  -1182  Optimised parameters:                       est. lower upper cyan_0            102.5565    NA    NA log_k_JCZ38        -3.4729    NA    NA log_k_J9Z38        -5.1533    NA    NA log_k_JSE76        -5.6669    NA    NA f_cyan_ilr_1        0.6665    NA    NA f_cyan_ilr_2        0.5191    NA    NA f_JCZ38_qlogis     37.0113    NA    NA log_k1             -1.8497    NA    NA log_k2             -4.4931    NA    NA g_qlogis           -0.6383    NA    NA a.1                 3.2397    NA    NA SD.log_k_JCZ38      1.4286    NA    NA SD.log_k_J9Z38      0.5312    NA    NA SD.log_k_JSE76      0.6627    NA    NA SD.f_cyan_ilr_1     0.3013    NA    NA SD.f_cyan_ilr_2     0.2980    NA    NA SD.f_JCZ38_qlogis   0.1637    NA    NA SD.log_k1           0.5069    NA    NA SD.log_k2           0.3828    NA    NA SD.g_qlogis         0.8641    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.4286    NA    NA SD.log_k_J9Z38    0.5312    NA    NA SD.log_k_JSE76    0.6627    NA    NA SD.f_cyan_ilr_1   0.3013    NA    NA SD.f_cyan_ilr_2   0.2980    NA    NA SD.f_JCZ38_qlogis 0.1637    NA    NA SD.log_k1         0.5069    NA    NA SD.log_k2         0.3828    NA    NA SD.g_qlogis       0.8641    NA    NA  Variance model:     est. lower upper a.1 3.24    NA    NA  Backtransformed parameters:                       est. lower upper cyan_0           1.026e+02    NA    NA k_JCZ38          3.103e-02    NA    NA k_J9Z38          5.780e-03    NA    NA k_JSE76          3.459e-03    NA    NA f_cyan_to_JCZ38  5.813e-01    NA    NA f_cyan_to_J9Z38  2.265e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA k1               1.573e-01    NA    NA k2               1.119e-02    NA    NA g                3.456e-01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.5813 cyan_J9Z38  0.2265 cyan_sink   0.1922 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   25.23 167.94    50.55   4.407   61.97 JCZ38  22.34  74.22       NA      NA      NA J9Z38 119.92 398.36       NA      NA      NA JSE76 200.41 665.76       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:38:54 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 674.81 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.3964        -3.3626        -4.9792        -5.8727         0.6814    f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2       g_qlogis          6.8713        13.6901        -1.9222        -4.5035        -0.7172   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.317       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       2.272       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.633       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       1.271       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6839 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 log_k1          0.000       0.000       0.000       0.000       0.0000 log_k2          0.000       0.000       0.000       0.000       0.0000 g_qlogis        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis cyan_0                 0.00           0.00 0.0000 0.0000    0.000 log_k_JCZ38            0.00           0.00 0.0000 0.0000    0.000 log_k_J9Z38            0.00           0.00 0.0000 0.0000    0.000 log_k_JSE76            0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_1           0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_2          11.95           0.00 0.0000 0.0000    0.000 f_JCZ38_qlogis         0.00          16.08 0.0000 0.0000    0.000 log_k1                 0.00           0.00 0.9496 0.0000    0.000 log_k2                 0.00           0.00 0.0000 0.5846    0.000 g_qlogis               0.00           0.00 0.0000 0.0000    1.719  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2398 2390  -1179  Optimised parameters:                        est. lower upper cyan_0            100.69709    NA    NA log_k_JCZ38        -3.46669    NA    NA log_k_J9Z38        -5.05076    NA    NA log_k_JSE76        -5.55558    NA    NA f_cyan_ilr_1        0.66045    NA    NA f_cyan_ilr_2        0.84275    NA    NA f_JCZ38_qlogis     64.22404    NA    NA log_k1             -2.17715    NA    NA log_k2             -4.55002    NA    NA g_qlogis           -0.55920    NA    NA a.1                 2.95785    NA    NA b.1                 0.04456    NA    NA SD.log_k_JCZ38      1.39881    NA    NA SD.log_k_J9Z38      0.67788    NA    NA SD.log_k_JSE76      0.52603    NA    NA SD.f_cyan_ilr_1     0.32490    NA    NA SD.f_cyan_ilr_2     0.53923    NA    NA SD.f_JCZ38_qlogis   2.75576    NA    NA SD.log_k2           0.30694    NA    NA SD.g_qlogis         0.83619    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.3988    NA    NA SD.log_k_J9Z38    0.6779    NA    NA SD.log_k_JSE76    0.5260    NA    NA SD.f_cyan_ilr_1   0.3249    NA    NA SD.f_cyan_ilr_2   0.5392    NA    NA SD.f_JCZ38_qlogis 2.7558    NA    NA SD.log_k2         0.3069    NA    NA SD.g_qlogis       0.8362    NA    NA  Variance model:        est. lower upper a.1 2.95785    NA    NA b.1 0.04456    NA    NA  Backtransformed parameters:                       est. lower upper cyan_0           1.007e+02    NA    NA k_JCZ38          3.122e-02    NA    NA k_J9Z38          6.404e-03    NA    NA k_JSE76          3.866e-03    NA    NA f_cyan_to_JCZ38  6.187e-01    NA    NA f_cyan_to_J9Z38  2.431e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA k1               1.134e-01    NA    NA k2               1.057e-02    NA    NA g                3.637e-01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.6187 cyan_J9Z38  0.2431 cyan_sink   0.1382 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   26.35 175.12    52.72   6.114    65.6 JCZ38  22.20  73.75       NA      NA      NA J9Z38 108.23 359.53       NA      NA      NA JSE76 179.30 595.62       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:36:26 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 527.639 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               102.0643               -2.8987               -2.7077  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.4717               -3.4008               -5.0024            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -5.8613                0.6855                1.2366         f_JCZ38_qlogis                13.7395   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 4.466          0.0000                 0.000 log_k_cyan_free             0.000          0.6158                 0.000 log_k_cyan_free_bound       0.000          0.0000                 1.463 log_k_cyan_bound_free       0.000          0.0000                 0.000 log_k_JCZ38                 0.000          0.0000                 0.000 log_k_J9Z38                 0.000          0.0000                 0.000 log_k_JSE76                 0.000          0.0000                 0.000 f_cyan_ilr_1                0.000          0.0000                 0.000 f_cyan_ilr_2                0.000          0.0000                 0.000 f_JCZ38_qlogis              0.000          0.0000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000       0.000       0.000       0.000 log_k_cyan_free                       0.000       0.000       0.000       0.000 log_k_cyan_free_bound                 0.000       0.000       0.000       0.000 log_k_cyan_bound_free                 1.058       0.000       0.000       0.000 log_k_JCZ38                           0.000       2.382       0.000       0.000 log_k_J9Z38                           0.000       0.000       1.595       0.000 log_k_JSE76                           0.000       0.000       0.000       1.245 f_cyan_ilr_1                          0.000       0.000       0.000       0.000 f_cyan_ilr_2                          0.000       0.000       0.000       0.000 f_JCZ38_qlogis                        0.000       0.000       0.000       0.000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_free_0                 0.0000         0.00           0.00 log_k_cyan_free             0.0000         0.00           0.00 log_k_cyan_free_bound       0.0000         0.00           0.00 log_k_cyan_bound_free       0.0000         0.00           0.00 log_k_JCZ38                 0.0000         0.00           0.00 log_k_J9Z38                 0.0000         0.00           0.00 log_k_JSE76                 0.0000         0.00           0.00 f_cyan_ilr_1                0.6852         0.00           0.00 f_cyan_ilr_2                0.0000         1.28           0.00 f_JCZ38_qlogis              0.0000         0.00          16.13  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2401 2394  -1181  Optimised parameters:                              est. lower upper cyan_free_0              102.8136    NA    NA log_k_cyan_free           -2.7935    NA    NA log_k_cyan_free_bound     -2.5440    NA    NA log_k_cyan_bound_free     -3.4303    NA    NA log_k_JCZ38               -3.5010    NA    NA log_k_J9Z38               -5.1226    NA    NA log_k_JSE76               -5.6314    NA    NA f_cyan_ilr_1               0.6609    NA    NA f_cyan_ilr_2               0.5085    NA    NA f_JCZ38_qlogis            44.0153    NA    NA a.1                        3.2318    NA    NA SD.log_k_cyan_free         0.3211    NA    NA SD.log_k_cyan_free_bound   0.8408    NA    NA SD.log_k_cyan_bound_free   0.5724    NA    NA SD.log_k_JCZ38             1.4925    NA    NA SD.log_k_J9Z38             0.5816    NA    NA SD.log_k_JSE76             0.6037    NA    NA SD.f_cyan_ilr_1            0.3115    NA    NA SD.f_cyan_ilr_2            0.3436    NA    NA SD.f_JCZ38_qlogis          4.8937    NA    NA  Correlation is not available  Random effects:                            est. lower upper SD.log_k_cyan_free       0.3211    NA    NA SD.log_k_cyan_free_bound 0.8408    NA    NA SD.log_k_cyan_bound_free 0.5724    NA    NA SD.log_k_JCZ38           1.4925    NA    NA SD.log_k_J9Z38           0.5816    NA    NA SD.log_k_JSE76           0.6037    NA    NA SD.f_cyan_ilr_1          0.3115    NA    NA SD.f_cyan_ilr_2          0.3436    NA    NA SD.f_JCZ38_qlogis        4.8937    NA    NA  Variance model:      est. lower upper a.1 3.232    NA    NA  Backtransformed parameters:                           est. lower upper cyan_free_0          1.028e+02    NA    NA k_cyan_free          6.120e-02    NA    NA k_cyan_free_bound    7.855e-02    NA    NA k_cyan_bound_free    3.238e-02    NA    NA k_JCZ38              3.017e-02    NA    NA k_J9Z38              5.961e-03    NA    NA k_JSE76              3.584e-03    NA    NA f_cyan_free_to_JCZ38 5.784e-01    NA    NA f_cyan_free_to_J9Z38 2.271e-01    NA    NA f_JCZ38_to_JSE76     1.000e+00    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.15973 0.01241 0.33124   Resulting formation fractions:                     ff cyan_free_JCZ38 0.5784 cyan_free_J9Z38 0.2271 cyan_free_sink  0.1945 cyan_free       1.0000 JCZ38_JSE76     1.0000 JCZ38_sink      0.0000  Estimated disappearance times:         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   24.51 153.18    46.11         4.34        55.87 JCZ38  22.98  76.33       NA           NA           NA J9Z38 116.28 386.29       NA           NA           NA JSE76 193.42 642.53       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:38:31 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 651.67 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               101.3964               -2.9881               -2.7949  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.4376               -3.3626               -4.9792            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -5.8727                0.6814                6.7399         f_JCZ38_qlogis                13.7395   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 5.317          0.0000                 0.000 log_k_cyan_free             0.000          0.7301                 0.000 log_k_cyan_free_bound       0.000          0.0000                 1.384 log_k_cyan_bound_free       0.000          0.0000                 0.000 log_k_JCZ38                 0.000          0.0000                 0.000 log_k_J9Z38                 0.000          0.0000                 0.000 log_k_JSE76                 0.000          0.0000                 0.000 f_cyan_ilr_1                0.000          0.0000                 0.000 f_cyan_ilr_2                0.000          0.0000                 0.000 f_JCZ38_qlogis              0.000          0.0000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000       0.000       0.000       0.000 log_k_cyan_free                       0.000       0.000       0.000       0.000 log_k_cyan_free_bound                 0.000       0.000       0.000       0.000 log_k_cyan_bound_free                 1.109       0.000       0.000       0.000 log_k_JCZ38                           0.000       2.272       0.000       0.000 log_k_J9Z38                           0.000       0.000       1.633       0.000 log_k_JSE76                           0.000       0.000       0.000       1.271 f_cyan_ilr_1                          0.000       0.000       0.000       0.000 f_cyan_ilr_2                          0.000       0.000       0.000       0.000 f_JCZ38_qlogis                        0.000       0.000       0.000       0.000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_free_0                 0.0000         0.00           0.00 log_k_cyan_free             0.0000         0.00           0.00 log_k_cyan_free_bound       0.0000         0.00           0.00 log_k_cyan_bound_free       0.0000         0.00           0.00 log_k_JCZ38                 0.0000         0.00           0.00 log_k_J9Z38                 0.0000         0.00           0.00 log_k_JSE76                 0.0000         0.00           0.00 f_cyan_ilr_1                0.6838         0.00           0.00 f_cyan_ilr_2                0.0000        11.69           0.00 f_JCZ38_qlogis              0.0000         0.00          16.13  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2400 2392  -1180  Optimised parameters:                               est. lower upper cyan_free_0              100.56004    NA    NA log_k_cyan_free           -3.12657    NA    NA log_k_cyan_free_bound     -3.16825    NA    NA log_k_cyan_bound_free     -3.66003    NA    NA log_k_JCZ38               -3.47278    NA    NA log_k_J9Z38               -5.06823    NA    NA log_k_JSE76               -5.54327    NA    NA f_cyan_ilr_1               0.66631    NA    NA f_cyan_ilr_2               0.82898    NA    NA f_JCZ38_qlogis            38.31115    NA    NA a.1                        2.98352    NA    NA b.1                        0.04388    NA    NA SD.log_k_cyan_free         0.49145    NA    NA SD.log_k_cyan_bound_free   0.27347    NA    NA SD.log_k_JCZ38             1.41193    NA    NA SD.log_k_J9Z38             0.66073    NA    NA SD.log_k_JSE76             0.55885    NA    NA SD.f_cyan_ilr_1            0.33020    NA    NA SD.f_cyan_ilr_2            0.51367    NA    NA SD.f_JCZ38_qlogis          5.52122    NA    NA  Correlation is not available  Random effects:                            est. lower upper SD.log_k_cyan_free       0.4914    NA    NA SD.log_k_cyan_bound_free 0.2735    NA    NA SD.log_k_JCZ38           1.4119    NA    NA SD.log_k_J9Z38           0.6607    NA    NA SD.log_k_JSE76           0.5589    NA    NA SD.f_cyan_ilr_1          0.3302    NA    NA SD.f_cyan_ilr_2          0.5137    NA    NA SD.f_JCZ38_qlogis        5.5212    NA    NA  Variance model:        est. lower upper a.1 2.98352    NA    NA b.1 0.04388    NA    NA  Backtransformed parameters:                           est. lower upper cyan_free_0          1.006e+02    NA    NA k_cyan_free          4.387e-02    NA    NA k_cyan_free_bound    4.208e-02    NA    NA k_cyan_bound_free    2.573e-02    NA    NA k_JCZ38              3.103e-02    NA    NA k_J9Z38              6.294e-03    NA    NA k_JSE76              3.914e-03    NA    NA f_cyan_free_to_JCZ38 6.188e-01    NA    NA f_cyan_free_to_J9Z38 2.412e-01    NA    NA f_JCZ38_to_JSE76     1.000e+00    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.10044 0.01124 0.36580   Resulting formation fractions:                     ff cyan_free_JCZ38 0.6188 cyan_free_J9Z38 0.2412 cyan_free_sink  0.1400 cyan_free       1.0000 JCZ38_JSE76     1.0000 JCZ38_sink      0.0000  Estimated disappearance times:         DT50  DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   26.05 164.4    49.48        6.901        61.67 JCZ38  22.34  74.2       NA           NA           NA J9Z38 110.14 365.9       NA           NA           NA JSE76 177.11 588.3       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:36:13 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan -            k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan -            k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 514.353 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.8845        -3.4495        -4.9355        -5.6040         0.6468    f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2         log_tb          1.2396         9.7220        -2.9079        -4.1810         1.7813   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.406        0.00        0.00       0.000       0.0000 log_k_JCZ38     0.000        2.33        0.00       0.000       0.0000 log_k_J9Z38     0.000        0.00        1.59       0.000       0.0000 log_k_JSE76     0.000        0.00        0.00       1.013       0.0000 f_cyan_ilr_1    0.000        0.00        0.00       0.000       0.6367 f_cyan_ilr_2    0.000        0.00        0.00       0.000       0.0000 f_JCZ38_qlogis  0.000        0.00        0.00       0.000       0.0000 log_k1          0.000        0.00        0.00       0.000       0.0000 log_k2          0.000        0.00        0.00       0.000       0.0000 log_tb          0.000        0.00        0.00       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb cyan_0                0.000           0.00 0.0000 0.0000 0.0000 log_k_JCZ38           0.000           0.00 0.0000 0.0000 0.0000 log_k_J9Z38           0.000           0.00 0.0000 0.0000 0.0000 log_k_JSE76           0.000           0.00 0.0000 0.0000 0.0000 f_cyan_ilr_1          0.000           0.00 0.0000 0.0000 0.0000 f_cyan_ilr_2          2.038           0.00 0.0000 0.0000 0.0000 f_JCZ38_qlogis        0.000          10.33 0.0000 0.0000 0.0000 log_k1                0.000           0.00 0.7006 0.0000 0.0000 log_k2                0.000           0.00 0.0000 0.8928 0.0000 log_tb                0.000           0.00 0.0000 0.0000 0.6773  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2427 2419  -1194  Optimised parameters:                       est.      lower      upper cyan_0            101.9660  1.005e+02  1.035e+02 log_k_JCZ38        -3.4698 -4.716e+00 -2.224e+00 log_k_J9Z38        -5.0947 -5.740e+00 -4.450e+00 log_k_JSE76        -5.5977 -6.321e+00 -4.875e+00 f_cyan_ilr_1        0.6595  3.734e-01  9.456e-01 f_cyan_ilr_2        0.5905  1.664e-01  1.015e+00 f_JCZ38_qlogis     25.8627 -4.224e+05  4.225e+05 log_k1             -3.0884 -3.453e+00 -2.723e+00 log_k2             -4.3877 -4.778e+00 -3.998e+00 log_tb              2.3057  1.715e+00  2.896e+00 a.1                 3.3228         NA         NA SD.log_k_JCZ38      1.4071         NA         NA SD.log_k_J9Z38      0.5774         NA         NA SD.log_k_JSE76      0.6214         NA         NA SD.f_cyan_ilr_1     0.3058         NA         NA SD.f_cyan_ilr_2     0.3470         NA         NA SD.f_JCZ38_qlogis   0.0644         NA         NA SD.log_k1           0.3994         NA         NA SD.log_k2           0.4373         NA         NA SD.log_tb           0.6419         NA         NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.4071    NA    NA SD.log_k_J9Z38    0.5774    NA    NA SD.log_k_JSE76    0.6214    NA    NA SD.f_cyan_ilr_1   0.3058    NA    NA SD.f_cyan_ilr_2   0.3470    NA    NA SD.f_JCZ38_qlogis 0.0644    NA    NA SD.log_k1         0.3994    NA    NA SD.log_k2         0.4373    NA    NA SD.log_tb         0.6419    NA    NA  Variance model:      est. lower upper a.1 3.323    NA    NA  Backtransformed parameters:                       est.     lower     upper cyan_0           1.020e+02 1.005e+02 1.035e+02 k_JCZ38          3.112e-02 8.951e-03 1.082e-01 k_J9Z38          6.129e-03 3.216e-03 1.168e-02 k_JSE76          3.706e-03 1.798e-03 7.639e-03 f_cyan_to_JCZ38  5.890e-01        NA        NA f_cyan_to_J9Z38  2.318e-01        NA        NA f_JCZ38_to_JSE76 1.000e+00 0.000e+00 1.000e+00 k1               4.558e-02 3.164e-02 6.565e-02 k2               1.243e-02 8.417e-03 1.835e-02 tb               1.003e+01 5.557e+00 1.811e+01  Resulting formation fractions:                    ff cyan_JCZ38  5.890e-01 cyan_J9Z38  2.318e-01 cyan_sink   1.793e-01 JCZ38_JSE76 1.000e+00 JCZ38_sink  5.861e-12  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   29.02 158.51    47.72   15.21   55.77 JCZ38  22.27  73.98       NA      NA      NA J9Z38 113.09 375.69       NA      NA      NA JSE76 187.01 621.23       NA      NA      NA"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"pathway-2","dir":"Articles > Prebuilt","previous_headings":"Appendix > Hierarchical fit listings","what":"Pathway 2","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"Hierarchical FOMC path 2 fit two-component error  Hierarchical DFOP path 2 fit constant variance  Hierarchical DFOP path 2 fit two-component error  Hierarchical SFORB path 2 fit constant variance  Hierarchical SFORB path 2 fit two-component error","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:47:30 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 505.533 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4477        -1.8631        -5.1087        -2.5114         0.6826    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta          4.7944        15.9616        13.1566        -0.1564         2.9781   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          7.701       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       1.448       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.724       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       3.659       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6356 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000 log_alpha       0.000       0.000       0.000       0.000       0.0000 log_beta        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta cyan_0                 0.00           0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          10.32           0.00           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          12.23           0.00    0.0000   0.0000 f_JSE76_qlogis         0.00           0.00          14.99    0.0000   0.0000 log_alpha              0.00           0.00           0.00    0.3924   0.0000 log_beta               0.00           0.00           0.00    0.0000   0.5639  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2249 2241  -1104  Optimised parameters:                        est.      lower     upper cyan_0            101.55265  9.920e+01  103.9059 log_k_JCZ38        -2.32302 -2.832e+00   -1.8142 log_k_J9Z38        -5.13082 -5.942e+00   -4.3199 log_k_JSE76        -3.01756 -4.262e+00   -1.7736 f_cyan_ilr_1        0.70850  3.657e-01    1.0513 f_cyan_ilr_2        0.95775  2.612e-01    1.6543 f_JCZ38_qlogis      3.86105  9.248e-01    6.7973 f_JSE76_qlogis      7.51583 -1.120e+02  127.0392 log_alpha          -0.15308 -4.508e-01    0.1446 log_beta            2.99165  2.711e+00    3.2720 a.1                 2.04034  1.843e+00    2.2382 b.1                 0.06924  5.749e-02    0.0810 SD.log_k_JCZ38      0.50818  1.390e-01    0.8774 SD.log_k_J9Z38      0.86597  2.652e-01    1.4667 SD.log_k_JSE76      1.38092  4.864e-01    2.2754 SD.f_cyan_ilr_1     0.38204  1.354e-01    0.6286 SD.f_cyan_ilr_2     0.55129  7.198e-02    1.0306 SD.f_JCZ38_qlogis   1.88457  1.711e-02    3.7520 SD.f_JSE76_qlogis   2.64018 -2.450e+03 2454.9447 SD.log_alpha        0.31860  1.047e-01    0.5325 SD.log_beta         0.24195  1.273e-02    0.4712  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 log_k_JCZ38    -0.0235                                                         log_k_J9Z38    -0.0442  0.0047                                                 log_k_JSE76    -0.0023  0.0966  0.0006                                         f_cyan_ilr_1   -0.0032  0.0070 -0.0536 -0.0001                                 f_cyan_ilr_2   -0.5189  0.0452  0.1152  0.0013 -0.0304                         f_JCZ38_qlogis  0.1088 -0.0848 -0.0240  0.0040 -0.0384 -0.2303                 f_JSE76_qlogis -0.0545  0.1315  0.0195  0.0020  0.0252  0.1737 -0.5939         log_alpha      -0.0445  0.0056  0.0261  0.0019 -0.0055  0.0586 -0.0239 -0.0284 log_beta       -0.2388  0.0163  0.0566  0.0040 -0.0078  0.2183 -0.0714 -0.0332                log_lph log_k_JCZ38            log_k_J9Z38            log_k_JSE76            f_cyan_ilr_1           f_cyan_ilr_2           f_JCZ38_qlogis         f_JSE76_qlogis         log_alpha              log_beta        0.2135  Random effects:                     est.      lower     upper SD.log_k_JCZ38    0.5082  1.390e-01    0.8774 SD.log_k_J9Z38    0.8660  2.652e-01    1.4667 SD.log_k_JSE76    1.3809  4.864e-01    2.2754 SD.f_cyan_ilr_1   0.3820  1.354e-01    0.6286 SD.f_cyan_ilr_2   0.5513  7.198e-02    1.0306 SD.f_JCZ38_qlogis 1.8846  1.711e-02    3.7520 SD.f_JSE76_qlogis 2.6402 -2.450e+03 2454.9447 SD.log_alpha      0.3186  1.047e-01    0.5325 SD.log_beta       0.2420  1.273e-02    0.4712  Variance model:        est.   lower upper a.1 2.04034 1.84252 2.238 b.1 0.06924 0.05749 0.081  Backtransformed parameters:                       est.     lower    upper cyan_0           1.016e+02 9.920e+01 103.9059 k_JCZ38          9.798e-02 5.890e-02   0.1630 k_J9Z38          5.912e-03 2.627e-03   0.0133 k_JSE76          4.892e-02 1.410e-02   0.1697 f_cyan_to_JCZ38  6.432e-01        NA       NA f_cyan_to_J9Z38  2.362e-01        NA       NA f_JCZ38_to_JSE76 9.794e-01 7.160e-01   0.9989 f_JSE76_to_JCZ38 9.995e-01 2.268e-49   1.0000 alpha            8.581e-01 6.371e-01   1.1556 beta             1.992e+01 1.505e+01  26.3646  Resulting formation fractions:                    ff cyan_JCZ38  0.6432301 cyan_J9Z38  0.2361657 cyan_sink   0.1206042 JCZ38_JSE76 0.9793879 JCZ38_sink  0.0206121 JSE76_JCZ38 0.9994559 JSE76_sink  0.0005441  Estimated disappearance times:          DT50   DT90 DT50back cyan   24.759 271.61    81.76 JCZ38   7.075  23.50       NA J9Z38 117.249 389.49       NA JSE76  14.169  47.07       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:48:20 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 555.413 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4380        -2.3107        -5.3123        -3.7120         0.6757    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          1.1439        13.1194        12.3492        -1.9317        -4.4557        g_qlogis         -0.5644   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          4.591      0.0000       0.000         0.0       0.0000 log_k_JCZ38     0.000      0.7966       0.000         0.0       0.0000 log_k_J9Z38     0.000      0.0000       1.561         0.0       0.0000 log_k_JSE76     0.000      0.0000       0.000         0.8       0.0000 f_cyan_ilr_1    0.000      0.0000       0.000         0.0       0.6349 f_cyan_ilr_2    0.000      0.0000       0.000         0.0       0.0000 f_JCZ38_qlogis  0.000      0.0000       0.000         0.0       0.0000 f_JSE76_qlogis  0.000      0.0000       0.000         0.0       0.0000 log_k1          0.000      0.0000       0.000         0.0       0.0000 log_k2          0.000      0.0000       0.000         0.0       0.0000 g_qlogis        0.000      0.0000       0.000         0.0       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                0.000           0.00           0.00  0.000 0.0000 log_k_JCZ38           0.000           0.00           0.00  0.000 0.0000 log_k_J9Z38           0.000           0.00           0.00  0.000 0.0000 log_k_JSE76           0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_1          0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_2          1.797           0.00           0.00  0.000 0.0000 f_JCZ38_qlogis        0.000          13.86           0.00  0.000 0.0000 f_JSE76_qlogis        0.000           0.00          13.91  0.000 0.0000 log_k1                0.000           0.00           0.00  1.106 0.0000 log_k2                0.000           0.00           0.00  0.000 0.6141 g_qlogis              0.000           0.00           0.00  0.000 0.0000                g_qlogis cyan_0            0.000 log_k_JCZ38       0.000 log_k_J9Z38       0.000 log_k_JSE76       0.000 f_cyan_ilr_1      0.000 f_cyan_ilr_2      0.000 f_JCZ38_qlogis    0.000 f_JSE76_qlogis    0.000 log_k1            0.000 log_k2            0.000 g_qlogis          1.595  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2288 2280  -1122  Optimised parameters:                       est.      lower      upper cyan_0            102.7204  1.014e+02  1.040e+02 log_k_JCZ38        -2.8925 -4.044e+00 -1.741e+00 log_k_J9Z38        -5.1430 -5.828e+00 -4.457e+00 log_k_JSE76        -3.5577 -4.174e+00 -2.941e+00 f_cyan_ilr_1        0.6929  3.788e-01  1.007e+00 f_cyan_ilr_2        0.6066  5.342e-02  1.160e+00 f_JCZ38_qlogis      9.8071 -2.819e+03  2.838e+03 f_JSE76_qlogis      2.2229  5.684e-01  3.877e+00 log_k1             -1.9339 -2.609e+00 -1.258e+00 log_k2             -4.4709 -4.935e+00 -4.007e+00 g_qlogis           -0.4987 -1.373e+00  3.757e-01 a.1                 2.7368  2.545e+00  2.928e+00 SD.log_k_JCZ38      1.2747  4.577e-01  2.092e+00 SD.log_k_J9Z38      0.6758  1.418e-01  1.210e+00 SD.log_k_JSE76      0.5869  1.169e-01  1.057e+00 SD.f_cyan_ilr_1     0.3392  1.161e-01  5.622e-01 SD.f_cyan_ilr_2     0.4200  8.501e-02  7.550e-01 SD.f_JCZ38_qlogis   0.8511 -1.137e+06  1.137e+06 SD.f_JSE76_qlogis   0.3767 -5.238e-01  1.277e+00 SD.log_k1           0.7475  2.601e-01  1.235e+00 SD.log_k2           0.5179  1.837e-01  8.521e-01 SD.g_qlogis         0.9817  3.553e-01  1.608e+00  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 log_k_JCZ38    -0.0351                                                         log_k_J9Z38    -0.0541  0.0043                                                 log_k_JSE76    -0.0078  0.0900 -0.0014                                         f_cyan_ilr_1   -0.0249  0.0268 -0.0962  0.0000                                 f_cyan_ilr_2   -0.3560  0.0848  0.1545 -0.0022  0.0463                         f_JCZ38_qlogis  0.2005 -0.1226 -0.0347  0.0514 -0.1840 -0.5906                 f_JSE76_qlogis -0.1638  0.1307  0.0266  0.0001  0.1645  0.5181 -0.9297         log_k1          0.0881 -0.0071  0.0005 -0.0070 -0.0064 -0.0346  0.0316 -0.0341 log_k2          0.0238 -0.0003  0.0082 -0.0022 -0.0017 -0.0017 -0.0002 -0.0076 g_qlogis        0.0198 -0.0002 -0.0109  0.0034  0.0017 -0.0176  0.0044  0.0051                log_k1  log_k2  log_k_JCZ38                    log_k_J9Z38                    log_k_JSE76                    f_cyan_ilr_1                   f_cyan_ilr_2                   f_JCZ38_qlogis                 f_JSE76_qlogis                 log_k1                         log_k2          0.0276         g_qlogis       -0.0283 -0.0309  Random effects:                     est.      lower     upper SD.log_k_JCZ38    1.2747  4.577e-01 2.092e+00 SD.log_k_J9Z38    0.6758  1.418e-01 1.210e+00 SD.log_k_JSE76    0.5869  1.169e-01 1.057e+00 SD.f_cyan_ilr_1   0.3392  1.161e-01 5.622e-01 SD.f_cyan_ilr_2   0.4200  8.501e-02 7.550e-01 SD.f_JCZ38_qlogis 0.8511 -1.137e+06 1.137e+06 SD.f_JSE76_qlogis 0.3767 -5.238e-01 1.277e+00 SD.log_k1         0.7475  2.601e-01 1.235e+00 SD.log_k2         0.5179  1.837e-01 8.521e-01 SD.g_qlogis       0.9817  3.553e-01 1.608e+00  Variance model:      est. lower upper a.1 2.737 2.545 2.928  Backtransformed parameters:                       est.     lower     upper cyan_0           102.72037 1.014e+02 104.00464 k_JCZ38            0.05544 1.752e-02   0.17539 k_J9Z38            0.00584 2.942e-03   0.01159 k_JSE76            0.02850 1.539e-02   0.05279 f_cyan_to_JCZ38    0.59995        NA        NA f_cyan_to_J9Z38    0.22519        NA        NA f_JCZ38_to_JSE76   0.99994 0.000e+00   1.00000 f_JSE76_to_JCZ38   0.90229 6.384e-01   0.97971 k1                 0.14459 7.357e-02   0.28414 k2                 0.01144 7.192e-03   0.01819 g                  0.37784 2.021e-01   0.59284  Resulting formation fractions:                    ff cyan_JCZ38  5.999e-01 cyan_J9Z38  2.252e-01 cyan_sink   1.749e-01 JCZ38_JSE76 9.999e-01 JCZ38_sink  5.506e-05 JSE76_JCZ38 9.023e-01 JSE76_sink  9.771e-02  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   21.93 159.83    48.11   4.794    60.6 JCZ38  12.50  41.53       NA      NA      NA J9Z38 118.69 394.27       NA      NA      NA JSE76  24.32  80.78       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:51:02 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 717.675 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.7393        -1.4493        -5.0118        -2.1269         0.6720    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          7.3362        13.4423        13.2659        -2.0061        -4.5527        g_qlogis         -0.5806   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.604        0.00       0.000       0.000       0.0000 log_k_JCZ38     0.000        2.77       0.000       0.000       0.0000 log_k_J9Z38     0.000        0.00       1.662       0.000       0.0000 log_k_JSE76     0.000        0.00       0.000       5.021       0.0000 f_cyan_ilr_1    0.000        0.00       0.000       0.000       0.6519 f_cyan_ilr_2    0.000        0.00       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000        0.00       0.000       0.000       0.0000 f_JSE76_qlogis  0.000        0.00       0.000       0.000       0.0000 log_k1          0.000        0.00       0.000       0.000       0.0000 log_k2          0.000        0.00       0.000       0.000       0.0000 g_qlogis        0.000        0.00       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                 0.00           0.00           0.00 0.0000 0.0000 log_k_JCZ38            0.00           0.00           0.00 0.0000 0.0000 log_k_J9Z38            0.00           0.00           0.00 0.0000 0.0000 log_k_JSE76            0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_1           0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_2          13.37           0.00           0.00 0.0000 0.0000 f_JCZ38_qlogis         0.00          14.21           0.00 0.0000 0.0000 f_JSE76_qlogis         0.00           0.00          14.58 0.0000 0.0000 log_k1                 0.00           0.00           0.00 0.8453 0.0000 log_k2                 0.00           0.00           0.00 0.0000 0.5969 g_qlogis               0.00           0.00           0.00 0.0000 0.0000                g_qlogis cyan_0             0.00 log_k_JCZ38        0.00 log_k_J9Z38        0.00 log_k_JSE76        0.00 f_cyan_ilr_1       0.00 f_cyan_ilr_2       0.00 f_JCZ38_qlogis     0.00 f_JSE76_qlogis     0.00 log_k1             0.00 log_k2             0.00 g_qlogis           1.69  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2234 2226  -1095  Optimised parameters:                        est.     lower     upper cyan_0            101.25496  99.14662 103.36331 log_k_JCZ38        -2.55593  -3.32972  -1.78215 log_k_J9Z38        -5.07103  -5.85423  -4.28783 log_k_JSE76        -3.25468  -4.17577  -2.33360 f_cyan_ilr_1        0.70139   0.35924   1.04355 f_cyan_ilr_2        1.07712   0.17789   1.97636 f_JCZ38_qlogis      3.57483   0.05990   7.08976 f_JSE76_qlogis      4.54884  -7.25628  16.35395 log_k1             -2.38201  -2.51639  -2.24763 log_k2             -4.66741  -4.91865  -4.41617 g_qlogis           -0.28446  -1.14192   0.57300 a.1                 2.05925   1.86481   2.25369 b.1                 0.06172   0.05062   0.07282 SD.log_k_JCZ38      0.81137   0.25296   1.36977 SD.log_k_J9Z38      0.83542   0.25395   1.41689 SD.log_k_JSE76      0.97903   0.30100   1.65707 SD.f_cyan_ilr_1     0.37878   0.13374   0.62382 SD.f_cyan_ilr_2     0.67274   0.10102   1.24446 SD.f_JCZ38_qlogis   1.35327  -0.42359   3.13012 SD.f_JSE76_qlogis   1.43956 -19.14972  22.02884 SD.log_k2           0.25329   0.07521   0.43138 SD.g_qlogis         0.95167   0.35149   1.55184  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 log_k_JCZ38    -0.0265                                                         log_k_J9Z38    -0.0392  0.0024                                                 log_k_JSE76     0.0011  0.1220 -0.0016                                         f_cyan_ilr_1   -0.0161  0.0217 -0.0552  0.0034                                 f_cyan_ilr_2   -0.4718  0.0829  0.1102  0.0042  0.0095                         f_JCZ38_qlogis  0.1609 -0.1318 -0.0277  0.0081 -0.1040 -0.4559                 f_JSE76_qlogis -0.1289  0.1494  0.0219  0.0012  0.1004  0.4309 -0.8543         log_k1          0.2618 -0.0739 -0.0167 -0.0148 -0.0444 -0.2768  0.3518 -0.3818 log_k2          0.0603 -0.0217  0.0174 -0.0058 -0.0197 -0.0533  0.0923 -0.1281 g_qlogis        0.0362  0.0115 -0.0111  0.0040  0.0095 -0.0116 -0.0439  0.0651                log_k1  log_k2  log_k_JCZ38                    log_k_J9Z38                    log_k_JSE76                    f_cyan_ilr_1                   f_cyan_ilr_2                   f_JCZ38_qlogis                 f_JSE76_qlogis                 log_k1                         log_k2          0.3269         g_qlogis       -0.1656 -0.0928  Random effects:                     est.     lower   upper SD.log_k_JCZ38    0.8114   0.25296  1.3698 SD.log_k_J9Z38    0.8354   0.25395  1.4169 SD.log_k_JSE76    0.9790   0.30100  1.6571 SD.f_cyan_ilr_1   0.3788   0.13374  0.6238 SD.f_cyan_ilr_2   0.6727   0.10102  1.2445 SD.f_JCZ38_qlogis 1.3533  -0.42359  3.1301 SD.f_JSE76_qlogis 1.4396 -19.14972 22.0288 SD.log_k2         0.2533   0.07521  0.4314 SD.g_qlogis       0.9517   0.35149  1.5518  Variance model:        est.   lower   upper a.1 2.05925 1.86481 2.25369 b.1 0.06172 0.05062 0.07282  Backtransformed parameters:                       est.     lower     upper cyan_0           1.013e+02 9.915e+01 103.36331 k_JCZ38          7.762e-02 3.580e-02   0.16828 k_J9Z38          6.276e-03 2.868e-03   0.01373 k_JSE76          3.859e-02 1.536e-02   0.09695 f_cyan_to_JCZ38  6.520e-01        NA        NA f_cyan_to_J9Z38  2.418e-01        NA        NA f_JCZ38_to_JSE76 9.727e-01 5.150e-01   0.99917 f_JSE76_to_JCZ38 9.895e-01 7.052e-04   1.00000 k1               9.236e-02 8.075e-02   0.10565 k2               9.397e-03 7.309e-03   0.01208 g                4.294e-01 2.420e-01   0.63945  Resulting formation fractions:                  ff cyan_JCZ38  0.65203 cyan_J9Z38  0.24181 cyan_sink   0.10616 JCZ38_JSE76 0.97274 JCZ38_sink  0.02726 JSE76_JCZ38 0.98953 JSE76_sink  0.01047  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   24.26 185.34    55.79   7.504   73.77 JCZ38   8.93  29.66       NA      NA      NA J9Z38 110.45 366.89       NA      NA      NA JSE76  17.96  59.66       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:48:15 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 550.772 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               102.4395               -2.7673               -2.8942  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6201               -2.3107               -5.3123            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.7120                0.6754                1.1448         f_JCZ38_qlogis        f_JSE76_qlogis                14.8408               15.4734   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 4.589          0.0000                  0.00 log_k_cyan_free             0.000          0.4849                  0.00 log_k_cyan_free_bound       0.000          0.0000                  1.62 log_k_cyan_bound_free       0.000          0.0000                  0.00 log_k_JCZ38                 0.000          0.0000                  0.00 log_k_J9Z38                 0.000          0.0000                  0.00 log_k_JSE76                 0.000          0.0000                  0.00 f_cyan_ilr_1                0.000          0.0000                  0.00 f_cyan_ilr_2                0.000          0.0000                  0.00 f_JCZ38_qlogis              0.000          0.0000                  0.00 f_JSE76_qlogis              0.000          0.0000                  0.00                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000         0.0 log_k_cyan_free                       0.000      0.0000       0.000         0.0 log_k_cyan_free_bound                 0.000      0.0000       0.000         0.0 log_k_cyan_bound_free                 1.197      0.0000       0.000         0.0 log_k_JCZ38                           0.000      0.7966       0.000         0.0 log_k_J9Z38                           0.000      0.0000       1.561         0.0 log_k_JSE76                           0.000      0.0000       0.000         0.8 f_cyan_ilr_1                          0.000      0.0000       0.000         0.0 f_cyan_ilr_2                          0.000      0.0000       0.000         0.0 f_JCZ38_qlogis                        0.000      0.0000       0.000         0.0 f_JSE76_qlogis                        0.000      0.0000       0.000         0.0                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000            0.0           0.00 log_k_cyan_free             0.0000        0.000            0.0           0.00 log_k_cyan_free_bound       0.0000        0.000            0.0           0.00 log_k_cyan_bound_free       0.0000        0.000            0.0           0.00 log_k_JCZ38                 0.0000        0.000            0.0           0.00 log_k_J9Z38                 0.0000        0.000            0.0           0.00 log_k_JSE76                 0.0000        0.000            0.0           0.00 f_cyan_ilr_1                0.6349        0.000            0.0           0.00 f_cyan_ilr_2                0.0000        1.797            0.0           0.00 f_JCZ38_qlogis              0.0000        0.000           15.6           0.00 f_JSE76_qlogis              0.0000        0.000            0.0          17.52  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2283 2275  -1120  Optimised parameters:                              est.     lower    upper cyan_free_0              102.6517 101.40815 103.8952 log_k_cyan_free           -2.8729  -3.18649  -2.5593 log_k_cyan_free_bound     -2.7803  -3.60525  -1.9552 log_k_cyan_bound_free     -3.5845  -4.16644  -3.0026 log_k_JCZ38               -2.3411  -2.89698  -1.7852 log_k_J9Z38               -5.2487  -6.01271  -4.4847 log_k_JSE76               -3.0259  -4.28274  -1.7690 f_cyan_ilr_1               0.7289   0.38214   1.0756 f_cyan_ilr_2               0.6891   0.18277   1.1954 f_JCZ38_qlogis             4.2162   0.47015   7.9622 f_JSE76_qlogis             5.8911 -20.19088  31.9730 a.1                        2.7159   2.52587   2.9060 SD.log_k_cyan_free         0.3354   0.10979   0.5610 SD.log_k_cyan_free_bound   0.9061   0.30969   1.5025 SD.log_k_cyan_bound_free   0.6376   0.21229   1.0628 SD.log_k_JCZ38             0.5499   0.14533   0.9545 SD.log_k_J9Z38             0.7457   0.15106   1.3404 SD.log_k_JSE76             1.3822   0.47329   2.2912 SD.f_cyan_ilr_1            0.3820   0.13280   0.6313 SD.f_cyan_ilr_2            0.4317   0.06803   0.7953 SD.f_JCZ38_qlogis          1.8258  -0.25423   3.9059 SD.f_JSE76_qlogis          2.2348 -83.33679  87.8065  Correlation:                        cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 log_k_cyan_free        0.1944                                                 log_k_cyan_free_bound  0.0815  0.0814                                         log_k_cyan_bound_free  0.0106  0.0426  0.0585                                 log_k_JCZ38           -0.0231 -0.0106 -0.0089     -0.0051                     log_k_J9Z38           -0.0457 -0.0108  0.0019      0.0129      0.0032         log_k_JSE76           -0.0054 -0.0024 -0.0017     -0.0005      0.1108  0.0009 f_cyan_ilr_1           0.0051 -0.0005 -0.0035     -0.0056      0.0131 -0.0967 f_cyan_ilr_2          -0.3182 -0.0771 -0.0309     -0.0038      0.0680  0.1643 f_JCZ38_qlogis         0.0834  0.0369  0.0302      0.0172     -0.1145 -0.0204 f_JSE76_qlogis        -0.0553 -0.0365 -0.0441     -0.0414      0.1579  0.0175                       l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_k_cyan_free                                       log_k_cyan_free_bound                                 log_k_cyan_bound_free                                 log_k_JCZ38                                           log_k_J9Z38                                           log_k_JSE76                                           f_cyan_ilr_1          -0.0002                         f_cyan_ilr_2           0.0020 -0.0415                 f_JCZ38_qlogis         0.0052 -0.0665 -0.3437         f_JSE76_qlogis         0.0066  0.0635  0.3491 -0.7487  Random effects:                            est.     lower   upper SD.log_k_cyan_free       0.3354   0.10979  0.5610 SD.log_k_cyan_free_bound 0.9061   0.30969  1.5025 SD.log_k_cyan_bound_free 0.6376   0.21229  1.0628 SD.log_k_JCZ38           0.5499   0.14533  0.9545 SD.log_k_J9Z38           0.7457   0.15106  1.3404 SD.log_k_JSE76           1.3822   0.47329  2.2912 SD.f_cyan_ilr_1          0.3820   0.13280  0.6313 SD.f_cyan_ilr_2          0.4317   0.06803  0.7953 SD.f_JCZ38_qlogis        1.8258  -0.25423  3.9059 SD.f_JSE76_qlogis        2.2348 -83.33679 87.8065  Variance model:      est. lower upper a.1 2.716 2.526 2.906  Backtransformed parameters:                           est.     lower     upper cyan_free_0          1.027e+02 1.014e+02 103.89517 k_cyan_free          5.654e-02 4.132e-02   0.07736 k_cyan_free_bound    6.202e-02 2.718e-02   0.14153 k_cyan_bound_free    2.775e-02 1.551e-02   0.04966 k_JCZ38              9.622e-02 5.519e-02   0.16777 k_J9Z38              5.254e-03 2.447e-03   0.01128 k_JSE76              4.852e-02 1.380e-02   0.17051 f_cyan_free_to_JCZ38 6.197e-01 5.643e-01   0.84429 f_cyan_free_to_J9Z38 2.211e-01 5.643e-01   0.84429 f_JCZ38_to_JSE76     9.855e-01 6.154e-01   0.99965 f_JSE76_to_JCZ38     9.972e-01 1.703e-09   1.00000  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.13466 0.01165 0.36490   Resulting formation fractions:                       ff cyan_free_JCZ38 0.619745 cyan_free_J9Z38 0.221083 cyan_free_sink  0.159172 cyan_free       1.000000 JCZ38_JSE76     0.985460 JCZ38_sink      0.014540 JSE76_JCZ38     0.997244 JSE76_sink      0.002756  Estimated disappearance times:          DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   23.293 158.67    47.77        5.147         59.5 JCZ38   7.203  23.93       NA           NA           NA J9Z38 131.918 438.22       NA           NA           NA JSE76  14.287  47.46       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:50:59 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 714.48 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               101.7511               -2.8370               -3.0162  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6600               -2.2988               -5.3129            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.6991                0.6722                4.8596         f_JCZ38_qlogis        f_JSE76_qlogis                13.4678               14.2149   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 5.629           0.000                 0.000 log_k_cyan_free             0.000           0.446                 0.000 log_k_cyan_free_bound       0.000           0.000                 1.449 log_k_cyan_bound_free       0.000           0.000                 0.000 log_k_JCZ38                 0.000           0.000                 0.000 log_k_J9Z38                 0.000           0.000                 0.000 log_k_JSE76                 0.000           0.000                 0.000 f_cyan_ilr_1                0.000           0.000                 0.000 f_cyan_ilr_2                0.000           0.000                 0.000 f_JCZ38_qlogis              0.000           0.000                 0.000 f_JSE76_qlogis              0.000           0.000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000      0.0000 log_k_cyan_free                       0.000      0.0000       0.000      0.0000 log_k_cyan_free_bound                 0.000      0.0000       0.000      0.0000 log_k_cyan_bound_free                 1.213      0.0000       0.000      0.0000 log_k_JCZ38                           0.000      0.7801       0.000      0.0000 log_k_J9Z38                           0.000      0.0000       1.575      0.0000 log_k_JSE76                           0.000      0.0000       0.000      0.8078 f_cyan_ilr_1                          0.000      0.0000       0.000      0.0000 f_cyan_ilr_2                          0.000      0.0000       0.000      0.0000 f_JCZ38_qlogis                        0.000      0.0000       0.000      0.0000 f_JSE76_qlogis                        0.000      0.0000       0.000      0.0000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000           0.00           0.00 log_k_cyan_free             0.0000        0.000           0.00           0.00 log_k_cyan_free_bound       0.0000        0.000           0.00           0.00 log_k_cyan_bound_free       0.0000        0.000           0.00           0.00 log_k_JCZ38                 0.0000        0.000           0.00           0.00 log_k_J9Z38                 0.0000        0.000           0.00           0.00 log_k_JSE76                 0.0000        0.000           0.00           0.00 f_cyan_ilr_1                0.6518        0.000           0.00           0.00 f_cyan_ilr_2                0.0000        9.981           0.00           0.00 f_JCZ38_qlogis              0.0000        0.000          14.26           0.00 f_JSE76_qlogis              0.0000        0.000           0.00          16.17  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2240 2231  -1098  Optimised parameters:                               est.      lower      upper cyan_free_0              100.73014  9.873e+01  1.027e+02 log_k_cyan_free           -3.19634 -3.641e+00 -2.752e+00 log_k_cyan_free_bound     -3.43533 -3.674e+00 -3.197e+00 log_k_cyan_bound_free     -3.83282 -4.163e+00 -3.503e+00 log_k_JCZ38               -2.51065 -3.225e+00 -1.796e+00 log_k_J9Z38               -5.02539 -5.825e+00 -4.226e+00 log_k_JSE76               -3.24777 -4.163e+00 -2.333e+00 f_cyan_ilr_1               0.70640  3.562e-01  1.057e+00 f_cyan_ilr_2               1.42704  3.170e-01  2.537e+00 f_JCZ38_qlogis             2.84779  1.042e+00  4.654e+00 f_JSE76_qlogis             8.63674 -6.407e+02  6.580e+02 a.1                        2.07082  1.877e+00  2.265e+00 b.1                        0.06227  5.098e-02  7.355e-02 SD.log_k_cyan_free         0.49674  1.865e-01  8.069e-01 SD.log_k_cyan_bound_free   0.28537  6.809e-02  5.027e-01 SD.log_k_JCZ38             0.74846  2.305e-01  1.266e+00 SD.log_k_J9Z38             0.86077  2.713e-01  1.450e+00 SD.log_k_JSE76             0.97613  3.030e-01  1.649e+00 SD.f_cyan_ilr_1            0.38994  1.382e-01  6.417e-01 SD.f_cyan_ilr_2            0.82869  3.917e-02  1.618e+00 SD.f_JCZ38_qlogis          1.05000 -2.808e-02  2.128e+00 SD.f_JSE76_qlogis          0.44681 -3.985e+05  3.985e+05  Correlation:                        cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 log_k_cyan_free        0.0936                                                 log_k_cyan_free_bound  0.1302  0.1627                                         log_k_cyan_bound_free  0.0029  0.0525  0.5181                                 log_k_JCZ38           -0.0116 -0.0077 -0.0430     -0.0236                     log_k_J9Z38           -0.0192 -0.0077 -0.0048      0.0229     -0.0005         log_k_JSE76            0.0007 -0.0020 -0.0134     -0.0072      0.1225 -0.0016 f_cyan_ilr_1          -0.0118 -0.0027 -0.0132     -0.0118      0.0127 -0.0505 f_cyan_ilr_2          -0.4643 -0.0762 -0.1245      0.0137      0.0497  0.1003 f_JCZ38_qlogis         0.0710  0.0371  0.1826      0.0925     -0.0869 -0.0130 f_JSE76_qlogis        -0.0367 -0.0270 -0.2274     -0.1865      0.1244  0.0098                       l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_k_cyan_free                                       log_k_cyan_free_bound                                 log_k_cyan_bound_free                                 log_k_JCZ38                                           log_k_J9Z38                                           log_k_JSE76                                           f_cyan_ilr_1           0.0036                         f_cyan_ilr_2           0.0050 -0.0201                 f_JCZ38_qlogis         0.0142 -0.0529 -0.2698         f_JSE76_qlogis         0.0064  0.0345  0.2015 -0.7058  Random effects:                            est.      lower     upper SD.log_k_cyan_free       0.4967  1.865e-01 8.069e-01 SD.log_k_cyan_bound_free 0.2854  6.809e-02 5.027e-01 SD.log_k_JCZ38           0.7485  2.305e-01 1.266e+00 SD.log_k_J9Z38           0.8608  2.713e-01 1.450e+00 SD.log_k_JSE76           0.9761  3.030e-01 1.649e+00 SD.f_cyan_ilr_1          0.3899  1.382e-01 6.417e-01 SD.f_cyan_ilr_2          0.8287  3.917e-02 1.618e+00 SD.f_JCZ38_qlogis        1.0500 -2.808e-02 2.128e+00 SD.f_JSE76_qlogis        0.4468 -3.985e+05 3.985e+05  Variance model:        est.   lower   upper a.1 2.07082 1.87680 2.26483 b.1 0.06227 0.05098 0.07355  Backtransformed parameters:                           est.      lower     upper cyan_free_0          1.007e+02  9.873e+01 102.72898 k_cyan_free          4.091e-02  2.623e-02   0.06382 k_cyan_free_bound    3.221e-02  2.537e-02   0.04090 k_cyan_bound_free    2.165e-02  1.557e-02   0.03011 k_JCZ38              8.122e-02  3.975e-02   0.16594 k_J9Z38              6.569e-03  2.954e-03   0.01461 k_JSE76              3.886e-02  1.556e-02   0.09703 f_cyan_free_to_JCZ38 6.785e-01  6.102e-01   0.97309 f_cyan_free_to_J9Z38 2.498e-01  6.102e-01   0.97309 f_JCZ38_to_JSE76     9.452e-01  7.392e-01   0.99056 f_JSE76_to_JCZ38     9.998e-01 5.580e-279   1.00000  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.08426 0.01051 0.41220   Resulting formation fractions:                        ff cyan_free_JCZ38 0.6784541 cyan_free_J9Z38 0.2498405 cyan_free_sink  0.0717054 cyan_free       1.0000000 JCZ38_JSE76     0.9452043 JCZ38_sink      0.0547957 JSE76_JCZ38     0.9998226 JSE76_sink      0.0001774  Estimated disappearance times:          DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   25.237 168.51    50.73        8.226        65.95 JCZ38   8.535  28.35       NA           NA           NA J9Z38 105.517 350.52       NA           NA           NA JSE76  17.837  59.25       NA           NA           NA"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"pathway-2-refined-fits","dir":"Articles > Prebuilt","previous_headings":"Appendix > Hierarchical fit listings","what":"Pathway 2, refined fits","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"Hierarchical FOMC path 2 fit reduced random effects, two-component error  Hierarchical DFOP path 2 fit reduced random effects, constant variance  Hierarchical DFOP path 2 fit reduced random effects, two-component error  Hierarchical SFORB path 2 fit reduced random effects, constant variance  Hierarchical SFORB path 2 fit reduced random effects, two-component error","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:04:10 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 786.038 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4477        -1.8631        -5.1087        -2.5114         0.6826    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta          4.7944        15.9616        13.1566        -0.1564         2.9781   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          7.701       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       1.448       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.724       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       3.659       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6356 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000 log_alpha       0.000       0.000       0.000       0.000       0.0000 log_beta        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta cyan_0                 0.00           0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          10.32           0.00           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          12.23           0.00    0.0000   0.0000 f_JSE76_qlogis         0.00           0.00          14.99    0.0000   0.0000 log_alpha              0.00           0.00           0.00    0.3924   0.0000 log_beta               0.00           0.00           0.00    0.0000   0.5639  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2249 2242  -1106  Optimised parameters:                      est.   lower  upper cyan_0          101.24524      NA     NA log_k_JCZ38      -2.85375      NA     NA log_k_J9Z38      -5.07729      NA     NA log_k_JSE76      -3.53511      NA     NA f_cyan_ilr_1      0.67478      NA     NA f_cyan_ilr_2      0.97152      NA     NA f_JCZ38_qlogis  213.48001      NA     NA f_JSE76_qlogis    2.02040      NA     NA log_alpha        -0.11041      NA     NA log_beta          3.06575      NA     NA a.1               2.05279 1.85495 2.2506 b.1               0.07116 0.05912 0.0832 SD.log_k_JCZ38    1.21713 0.44160 1.9927 SD.log_k_J9Z38    0.88268 0.27541 1.4900 SD.log_k_JSE76    0.59452 0.15005 1.0390 SD.f_cyan_ilr_1   0.35370 0.12409 0.5833 SD.f_cyan_ilr_2   0.78186 0.18547 1.3782 SD.log_alpha      0.27781 0.08168 0.4739 SD.log_beta       0.32608 0.06490 0.5873  Correlation is not available  Random effects:                   est.   lower  upper SD.log_k_JCZ38  1.2171 0.44160 1.9927 SD.log_k_J9Z38  0.8827 0.27541 1.4900 SD.log_k_JSE76  0.5945 0.15005 1.0390 SD.f_cyan_ilr_1 0.3537 0.12409 0.5833 SD.f_cyan_ilr_2 0.7819 0.18547 1.3782 SD.log_alpha    0.2778 0.08168 0.4739 SD.log_beta     0.3261 0.06490 0.5873  Variance model:        est.   lower  upper a.1 2.05279 1.85495 2.2506 b.1 0.07116 0.05912 0.0832  Backtransformed parameters:                       est. lower upper cyan_0           1.012e+02    NA    NA k_JCZ38          5.763e-02    NA    NA k_J9Z38          6.237e-03    NA    NA k_JSE76          2.916e-02    NA    NA f_cyan_to_JCZ38  6.354e-01    NA    NA f_cyan_to_J9Z38  2.447e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA f_JSE76_to_JCZ38 8.829e-01    NA    NA alpha            8.955e-01    NA    NA beta             2.145e+01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.6354 cyan_J9Z38  0.2447 cyan_sink   0.1200 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000 JSE76_JCZ38 0.8829 JSE76_sink  0.1171  Estimated disappearance times:         DT50   DT90 DT50back cyan   25.07 259.21    78.03 JCZ38  12.03  39.96       NA J9Z38 111.14 369.19       NA JSE76  23.77  78.98       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:05:56 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 892.139 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4380        -2.3107        -5.3123        -3.7120         0.6757    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          1.1439        13.1194        12.3492        -1.9317        -4.4557        g_qlogis         -0.5644   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          4.591      0.0000       0.000         0.0       0.0000 log_k_JCZ38     0.000      0.7966       0.000         0.0       0.0000 log_k_J9Z38     0.000      0.0000       1.561         0.0       0.0000 log_k_JSE76     0.000      0.0000       0.000         0.8       0.0000 f_cyan_ilr_1    0.000      0.0000       0.000         0.0       0.6349 f_cyan_ilr_2    0.000      0.0000       0.000         0.0       0.0000 f_JCZ38_qlogis  0.000      0.0000       0.000         0.0       0.0000 f_JSE76_qlogis  0.000      0.0000       0.000         0.0       0.0000 log_k1          0.000      0.0000       0.000         0.0       0.0000 log_k2          0.000      0.0000       0.000         0.0       0.0000 g_qlogis        0.000      0.0000       0.000         0.0       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                0.000           0.00           0.00  0.000 0.0000 log_k_JCZ38           0.000           0.00           0.00  0.000 0.0000 log_k_J9Z38           0.000           0.00           0.00  0.000 0.0000 log_k_JSE76           0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_1          0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_2          1.797           0.00           0.00  0.000 0.0000 f_JCZ38_qlogis        0.000          13.86           0.00  0.000 0.0000 f_JSE76_qlogis        0.000           0.00          13.91  0.000 0.0000 log_k1                0.000           0.00           0.00  1.106 0.0000 log_k2                0.000           0.00           0.00  0.000 0.6141 g_qlogis              0.000           0.00           0.00  0.000 0.0000                g_qlogis cyan_0            0.000 log_k_JCZ38       0.000 log_k_J9Z38       0.000 log_k_JSE76       0.000 f_cyan_ilr_1      0.000 f_cyan_ilr_2      0.000 f_JCZ38_qlogis    0.000 f_JSE76_qlogis    0.000 log_k1            0.000 log_k2            0.000 g_qlogis          1.595  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2282 2274  -1121  Optimised parameters:                      est.   lower  upper cyan_0           102.6036      NA     NA log_k_JCZ38       -2.9348      NA     NA log_k_J9Z38       -5.1617      NA     NA log_k_JSE76       -3.6396      NA     NA f_cyan_ilr_1       0.6991      NA     NA f_cyan_ilr_2       0.6341      NA     NA f_JCZ38_qlogis  4232.3011      NA     NA f_JSE76_qlogis     1.9658      NA     NA log_k1            -1.9503      NA     NA log_k2            -4.4745      NA     NA g_qlogis          -0.4967      NA     NA a.1                2.7461 2.59274 2.8994 SD.log_k_JCZ38     1.3178 0.47602 2.1596 SD.log_k_J9Z38     0.7022 0.15061 1.2538 SD.log_k_JSE76     0.6566 0.15613 1.1570 SD.f_cyan_ilr_1    0.3409 0.11666 0.5652 SD.f_cyan_ilr_2    0.4385 0.09482 0.7821 SD.log_k1          0.7381 0.25599 1.2202 SD.log_k2          0.5133 0.18152 0.8450 SD.g_qlogis        0.9866 0.35681 1.6164  Correlation is not available  Random effects:                   est.   lower  upper SD.log_k_JCZ38  1.3178 0.47602 2.1596 SD.log_k_J9Z38  0.7022 0.15061 1.2538 SD.log_k_JSE76  0.6566 0.15613 1.1570 SD.f_cyan_ilr_1 0.3409 0.11666 0.5652 SD.f_cyan_ilr_2 0.4385 0.09482 0.7821 SD.log_k1       0.7381 0.25599 1.2202 SD.log_k2       0.5133 0.18152 0.8450 SD.g_qlogis     0.9866 0.35681 1.6164  Variance model:      est. lower upper a.1 2.746 2.593 2.899  Backtransformed parameters:                       est. lower upper cyan_0           1.026e+02    NA    NA k_JCZ38          5.314e-02    NA    NA k_J9Z38          5.732e-03    NA    NA k_JSE76          2.626e-02    NA    NA f_cyan_to_JCZ38  6.051e-01    NA    NA f_cyan_to_J9Z38  2.251e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA f_JSE76_to_JCZ38 8.772e-01    NA    NA k1               1.422e-01    NA    NA k2               1.140e-02    NA    NA g                3.783e-01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.6051 cyan_J9Z38  0.2251 cyan_sink   0.1698 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000 JSE76_JCZ38 0.8772 JSE76_sink  0.1228  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   22.05 160.35    48.27   4.873   60.83 JCZ38  13.04  43.33       NA      NA      NA J9Z38 120.93 401.73       NA      NA      NA JSE76  26.39  87.68       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:02 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 898.534 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.7393        -1.4493        -5.0118        -2.1269         0.6720    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          7.3362        13.4423        13.2659        -2.0061        -4.5527        g_qlogis         -0.5806   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.604        0.00       0.000       0.000       0.0000 log_k_JCZ38     0.000        2.77       0.000       0.000       0.0000 log_k_J9Z38     0.000        0.00       1.662       0.000       0.0000 log_k_JSE76     0.000        0.00       0.000       5.021       0.0000 f_cyan_ilr_1    0.000        0.00       0.000       0.000       0.6519 f_cyan_ilr_2    0.000        0.00       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000        0.00       0.000       0.000       0.0000 f_JSE76_qlogis  0.000        0.00       0.000       0.000       0.0000 log_k1          0.000        0.00       0.000       0.000       0.0000 log_k2          0.000        0.00       0.000       0.000       0.0000 g_qlogis        0.000        0.00       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                 0.00           0.00           0.00 0.0000 0.0000 log_k_JCZ38            0.00           0.00           0.00 0.0000 0.0000 log_k_J9Z38            0.00           0.00           0.00 0.0000 0.0000 log_k_JSE76            0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_1           0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_2          13.37           0.00           0.00 0.0000 0.0000 f_JCZ38_qlogis         0.00          14.21           0.00 0.0000 0.0000 f_JSE76_qlogis         0.00           0.00          14.58 0.0000 0.0000 log_k1                 0.00           0.00           0.00 0.8453 0.0000 log_k2                 0.00           0.00           0.00 0.0000 0.5969 g_qlogis               0.00           0.00           0.00 0.0000 0.0000                g_qlogis cyan_0             0.00 log_k_JCZ38        0.00 log_k_J9Z38        0.00 log_k_JSE76        0.00 f_cyan_ilr_1       0.00 f_cyan_ilr_2       0.00 f_JCZ38_qlogis     0.00 f_JSE76_qlogis     0.00 log_k1             0.00 log_k2             0.00 g_qlogis           1.69  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2237 2229  -1099  Optimised parameters:                      est.   lower   upper cyan_0          101.00243      NA      NA log_k_JCZ38      -2.80828      NA      NA log_k_J9Z38      -5.04449      NA      NA log_k_JSE76      -3.66981      NA      NA f_cyan_ilr_1      0.72564      NA      NA f_cyan_ilr_2      1.37978      NA      NA f_JCZ38_qlogis    1.98726      NA      NA f_JSE76_qlogis  414.80884      NA      NA log_k1           -2.38601      NA      NA log_k2           -4.63632      NA      NA g_qlogis         -0.33920      NA      NA a.1               2.10837 1.91261 2.30413 b.1               0.06223 0.05085 0.07361 SD.log_k_JCZ38    1.30902 0.48128 2.13675 SD.log_k_J9Z38    0.83882 0.25790 1.41974 SD.log_k_JSE76    0.58104 0.14201 1.02008 SD.f_cyan_ilr_1   0.35421 0.12398 0.58443 SD.f_cyan_ilr_2   0.79373 0.12007 1.46739 SD.log_k2         0.27476 0.08557 0.46394 SD.g_qlogis       0.96170 0.35463 1.56878  Correlation is not available  Random effects:                   est.   lower  upper SD.log_k_JCZ38  1.3090 0.48128 2.1367 SD.log_k_J9Z38  0.8388 0.25790 1.4197 SD.log_k_JSE76  0.5810 0.14201 1.0201 SD.f_cyan_ilr_1 0.3542 0.12398 0.5844 SD.f_cyan_ilr_2 0.7937 0.12007 1.4674 SD.log_k2       0.2748 0.08557 0.4639 SD.g_qlogis     0.9617 0.35463 1.5688  Variance model:        est.   lower   upper a.1 2.10837 1.91261 2.30413 b.1 0.06223 0.05085 0.07361  Backtransformed parameters:                       est. lower upper cyan_0           1.010e+02    NA    NA k_JCZ38          6.031e-02    NA    NA k_J9Z38          6.445e-03    NA    NA k_JSE76          2.548e-02    NA    NA f_cyan_to_JCZ38  6.808e-01    NA    NA f_cyan_to_J9Z38  2.440e-01    NA    NA f_JCZ38_to_JSE76 8.795e-01    NA    NA f_JSE76_to_JCZ38 1.000e+00    NA    NA k1               9.200e-02    NA    NA k2               9.693e-03    NA    NA g                4.160e-01    NA    NA  Resulting formation fractions:                  ff cyan_JCZ38  0.68081 cyan_J9Z38  0.24398 cyan_sink   0.07521 JCZ38_JSE76 0.87945 JCZ38_sink  0.12055 JSE76_JCZ38 1.00000 JSE76_sink  0.00000  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   25.00 182.05     54.8   7.535   71.51 JCZ38  11.49  38.18       NA      NA      NA J9Z38 107.55 357.28       NA      NA      NA JSE76  27.20  90.36       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:05:52 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 888.333 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               102.4395               -2.7673               -2.8942  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6201               -2.3107               -5.3123            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.7120                0.6754                1.1448         f_JCZ38_qlogis        f_JSE76_qlogis                14.8408               15.4734   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 4.589          0.0000                  0.00 log_k_cyan_free             0.000          0.4849                  0.00 log_k_cyan_free_bound       0.000          0.0000                  1.62 log_k_cyan_bound_free       0.000          0.0000                  0.00 log_k_JCZ38                 0.000          0.0000                  0.00 log_k_J9Z38                 0.000          0.0000                  0.00 log_k_JSE76                 0.000          0.0000                  0.00 f_cyan_ilr_1                0.000          0.0000                  0.00 f_cyan_ilr_2                0.000          0.0000                  0.00 f_JCZ38_qlogis              0.000          0.0000                  0.00 f_JSE76_qlogis              0.000          0.0000                  0.00                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000         0.0 log_k_cyan_free                       0.000      0.0000       0.000         0.0 log_k_cyan_free_bound                 0.000      0.0000       0.000         0.0 log_k_cyan_bound_free                 1.197      0.0000       0.000         0.0 log_k_JCZ38                           0.000      0.7966       0.000         0.0 log_k_J9Z38                           0.000      0.0000       1.561         0.0 log_k_JSE76                           0.000      0.0000       0.000         0.8 f_cyan_ilr_1                          0.000      0.0000       0.000         0.0 f_cyan_ilr_2                          0.000      0.0000       0.000         0.0 f_JCZ38_qlogis                        0.000      0.0000       0.000         0.0 f_JSE76_qlogis                        0.000      0.0000       0.000         0.0                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000            0.0           0.00 log_k_cyan_free             0.0000        0.000            0.0           0.00 log_k_cyan_free_bound       0.0000        0.000            0.0           0.00 log_k_cyan_bound_free       0.0000        0.000            0.0           0.00 log_k_JCZ38                 0.0000        0.000            0.0           0.00 log_k_J9Z38                 0.0000        0.000            0.0           0.00 log_k_JSE76                 0.0000        0.000            0.0           0.00 f_cyan_ilr_1                0.6349        0.000            0.0           0.00 f_cyan_ilr_2                0.0000        1.797            0.0           0.00 f_JCZ38_qlogis              0.0000        0.000           15.6           0.00 f_JSE76_qlogis              0.0000        0.000            0.0          17.52  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2280 2272  -1120  Optimised parameters:                               est.   lower  upper cyan_free_0               102.6532      NA     NA log_k_cyan_free            -2.8547      NA     NA log_k_cyan_free_bound      -2.7004      NA     NA log_k_cyan_bound_free      -3.5078      NA     NA log_k_JCZ38                -2.9255      NA     NA log_k_J9Z38                -5.1089      NA     NA log_k_JSE76                -3.6263      NA     NA f_cyan_ilr_1                0.6873      NA     NA f_cyan_ilr_2                0.6498      NA     NA f_JCZ38_qlogis           3624.2149      NA     NA f_JSE76_qlogis              1.9991      NA     NA a.1                         2.7472 2.55559 2.9388 SD.log_k_cyan_free          0.3227 0.10296 0.5423 SD.log_k_cyan_free_bound    0.8757 0.29525 1.4562 SD.log_k_cyan_bound_free    0.6128 0.20220 1.0233 SD.log_k_JCZ38              1.3431 0.48474 2.2014 SD.log_k_J9Z38              0.6881 0.14714 1.2291 SD.log_k_JSE76              0.6461 0.15321 1.1390 SD.f_cyan_ilr_1             0.3361 0.11376 0.5585 SD.f_cyan_ilr_2             0.4286 0.08419 0.7730  Correlation is not available  Random effects:                            est.   lower  upper SD.log_k_cyan_free       0.3227 0.10296 0.5423 SD.log_k_cyan_free_bound 0.8757 0.29525 1.4562 SD.log_k_cyan_bound_free 0.6128 0.20220 1.0233 SD.log_k_JCZ38           1.3431 0.48474 2.2014 SD.log_k_J9Z38           0.6881 0.14714 1.2291 SD.log_k_JSE76           0.6461 0.15321 1.1390 SD.f_cyan_ilr_1          0.3361 0.11376 0.5585 SD.f_cyan_ilr_2          0.4286 0.08419 0.7730  Variance model:      est. lower upper a.1 2.747 2.556 2.939  Backtransformed parameters:                           est. lower upper cyan_free_0          1.027e+02    NA    NA k_cyan_free          5.758e-02    NA    NA k_cyan_free_bound    6.718e-02    NA    NA k_cyan_bound_free    2.996e-02    NA    NA k_JCZ38              5.364e-02    NA    NA k_J9Z38              6.042e-03    NA    NA k_JSE76              2.662e-02    NA    NA f_cyan_free_to_JCZ38 6.039e-01    NA    NA f_cyan_free_to_J9Z38 2.285e-01    NA    NA f_JCZ38_to_JSE76     1.000e+00    NA    NA f_JSE76_to_JCZ38     8.807e-01    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g   0.1426  0.0121  0.3484   Resulting formation fractions:                     ff cyan_free_JCZ38 0.6039 cyan_free_J9Z38 0.2285 cyan_free_sink  0.1676 cyan_free       1.0000 JCZ38_JSE76     1.0000 JCZ38_sink      0.0000 JSE76_JCZ38     0.8807 JSE76_sink      0.1193  Estimated disappearance times:         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   23.84 154.95    46.65         4.86        57.31 JCZ38  12.92  42.93       NA           NA           NA J9Z38 114.71 381.07       NA           NA           NA JSE76  26.04  86.51       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:21 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 916.619 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               101.7511               -2.8370               -3.0162  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6600               -2.2988               -5.3129            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.6991                0.6722                4.8596         f_JCZ38_qlogis        f_JSE76_qlogis                13.4678               14.2149   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 5.629           0.000                 0.000 log_k_cyan_free             0.000           0.446                 0.000 log_k_cyan_free_bound       0.000           0.000                 1.449 log_k_cyan_bound_free       0.000           0.000                 0.000 log_k_JCZ38                 0.000           0.000                 0.000 log_k_J9Z38                 0.000           0.000                 0.000 log_k_JSE76                 0.000           0.000                 0.000 f_cyan_ilr_1                0.000           0.000                 0.000 f_cyan_ilr_2                0.000           0.000                 0.000 f_JCZ38_qlogis              0.000           0.000                 0.000 f_JSE76_qlogis              0.000           0.000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000      0.0000 log_k_cyan_free                       0.000      0.0000       0.000      0.0000 log_k_cyan_free_bound                 0.000      0.0000       0.000      0.0000 log_k_cyan_bound_free                 1.213      0.0000       0.000      0.0000 log_k_JCZ38                           0.000      0.7801       0.000      0.0000 log_k_J9Z38                           0.000      0.0000       1.575      0.0000 log_k_JSE76                           0.000      0.0000       0.000      0.8078 f_cyan_ilr_1                          0.000      0.0000       0.000      0.0000 f_cyan_ilr_2                          0.000      0.0000       0.000      0.0000 f_JCZ38_qlogis                        0.000      0.0000       0.000      0.0000 f_JSE76_qlogis                        0.000      0.0000       0.000      0.0000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000           0.00           0.00 log_k_cyan_free             0.0000        0.000           0.00           0.00 log_k_cyan_free_bound       0.0000        0.000           0.00           0.00 log_k_cyan_bound_free       0.0000        0.000           0.00           0.00 log_k_JCZ38                 0.0000        0.000           0.00           0.00 log_k_J9Z38                 0.0000        0.000           0.00           0.00 log_k_JSE76                 0.0000        0.000           0.00           0.00 f_cyan_ilr_1                0.6518        0.000           0.00           0.00 f_cyan_ilr_2                0.0000        9.981           0.00           0.00 f_JCZ38_qlogis              0.0000        0.000          14.26           0.00 f_JSE76_qlogis              0.0000        0.000           0.00          16.17  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2241 2233  -1101  Optimised parameters:                               est.   lower   upper cyan_free_0              100.95469      NA      NA log_k_cyan_free           -3.18706      NA      NA log_k_cyan_free_bound     -3.38455      NA      NA log_k_cyan_bound_free     -3.75788      NA      NA log_k_JCZ38               -2.77024      NA      NA log_k_J9Z38               -5.03665      NA      NA log_k_JSE76               -3.60289      NA      NA f_cyan_ilr_1               0.72263      NA      NA f_cyan_ilr_2               1.45352      NA      NA f_JCZ38_qlogis             2.00778      NA      NA f_JSE76_qlogis           941.58570      NA      NA a.1                        2.11130 1.91479 2.30780 b.1                        0.06299 0.05152 0.07445 SD.log_k_cyan_free         0.50098 0.18805 0.81390 SD.log_k_cyan_bound_free   0.31671 0.08467 0.54875 SD.log_k_JCZ38             1.25865 0.45932 2.05798 SD.log_k_J9Z38             0.86833 0.27222 1.46444 SD.log_k_JSE76             0.59325 0.14711 1.03940 SD.f_cyan_ilr_1            0.35705 0.12521 0.58890 SD.f_cyan_ilr_2            0.88541 0.13797 1.63286  Correlation is not available  Random effects:                            est.   lower  upper SD.log_k_cyan_free       0.5010 0.18805 0.8139 SD.log_k_cyan_bound_free 0.3167 0.08467 0.5487 SD.log_k_JCZ38           1.2587 0.45932 2.0580 SD.log_k_J9Z38           0.8683 0.27222 1.4644 SD.log_k_JSE76           0.5933 0.14711 1.0394 SD.f_cyan_ilr_1          0.3571 0.12521 0.5889 SD.f_cyan_ilr_2          0.8854 0.13797 1.6329  Variance model:        est.   lower   upper a.1 2.11130 1.91479 2.30780 b.1 0.06299 0.05152 0.07445  Backtransformed parameters:                           est. lower upper cyan_free_0          1.010e+02    NA    NA k_cyan_free          4.129e-02    NA    NA k_cyan_free_bound    3.389e-02    NA    NA k_cyan_bound_free    2.333e-02    NA    NA k_JCZ38              6.265e-02    NA    NA k_J9Z38              6.495e-03    NA    NA k_JSE76              2.724e-02    NA    NA f_cyan_free_to_JCZ38 6.844e-01    NA    NA f_cyan_free_to_J9Z38 2.463e-01    NA    NA f_JCZ38_to_JSE76     8.816e-01    NA    NA f_JSE76_to_JCZ38     1.000e+00    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.08751 0.01101 0.39586   Resulting formation fractions:                      ff cyan_free_JCZ38 0.68444 cyan_free_J9Z38 0.24633 cyan_free_sink  0.06923 cyan_free       1.00000 JCZ38_JSE76     0.88161 JCZ38_sink      0.11839 JSE76_JCZ38     1.00000 JSE76_sink      0.00000  Estimated disappearance times:         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   25.36 163.36    49.18        7.921        62.95 JCZ38  11.06  36.75       NA           NA           NA J9Z38 106.71 354.49       NA           NA           NA JSE76  25.44  84.51       NA           NA           NA"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] sass_0.4.9        generics_0.1.3    lattice_0.22-6    digest_0.6.37      [5] magrittr_2.0.3    evaluate_1.0.3    grid_4.5.0        fastmap_1.2.0      [9] cellranger_1.1.0  jsonlite_1.9.0    processx_3.8.6    pkgbuild_1.4.6    [13] deSolve_1.40      mclust_6.1.1      ps_1.9.0          gridExtra_2.3     [17] scales_1.3.0      codetools_0.2-20  textshaping_1.0.0 jquerylib_0.1.4   [21] cli_3.6.4         rlang_1.1.5       munsell_0.5.1     cachem_1.1.0      [25] yaml_2.3.10       inline_0.3.21     tools_4.5.0       dplyr_1.1.4       [29] colorspace_2.1-1  ggplot2_3.5.1     vctrs_0.6.5       R6_2.6.1          [33] zoo_1.8-13        lifecycle_1.0.4   fs_1.6.5          htmlwidgets_1.6.4 [37] MASS_7.3-65       ragg_1.3.3        pkgconfig_2.0.3   desc_1.4.3        [41] callr_3.7.6       pkgdown_2.1.1     pillar_1.10.1     bslib_0.9.0       [45] gtable_0.3.6      glue_1.8.0        systemfonts_1.2.1 xfun_0.51         [49] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.1  htmltools_0.5.8.1 [53] nlme_3.1-168      compiler_4.5.0    readxl_1.4.4"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"purpose document demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package. assembled course work package 1.1 Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) German Environment Agency carried 2022 2023. mkin package used version 1.2.10. contains test data functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"data","dir":"Articles > Prebuilt","previous_headings":"","what":"Data","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"test data available mkin package object class mkindsg (mkin dataset group) identifier dimethenamid_2018. following preprocessing steps still necessary: data available enantiomer dimethenamid-P (DMTAP) renamed substance name data racemic mixture dimethenamid (DMTA). reason difference degradation behaviour identified EU risk assessment. data transformation products unnecessary columns discarded observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters independent temperature Finally, datasets observed soil (Elliot 1 Elliot 2) combined, resulting dimethenamid (DMTA) data six soils. following commented R code performs preprocessing. following tables show 6 datasets. Dataset Calke Dataset Borstel Dataset Flaach Dataset BBA 2.2 Dataset BBA 2.3 Dataset Elliot","code":"# Apply a function to each of the seven datasets in the mkindsg object to create a list dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data                     # Get a dataset   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"              # Rename DMTAP to DMTA   ds_i <- subset(ds_i, name == \"DMTA\", c(\"name\", \"time\", \"value\")) # Select data   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]  # Normalise time   ds_i                                                       # Return the dataset })  # Use dataset titles as names for the list elements names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)  # Combine data for Elliot soil to obtain a named list with six elements dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) # dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL for (ds_name in names(dmta_ds)) {     print(kable(mkin_long_to_wide(dmta_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       label = paste0(\"tab:\", ds_name), booktabs = TRUE))     cat(\"\\n\\\\clearpage\\n\") }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"separate-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Separate evaluations","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"order obtain suitable starting parameters NLHM fits, separate fits four models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. table , OK indicates convergence, C indicates failure converge. separate fits constant variance converged, sole exception HS fit BBA 2.2 data. prepare fitting NLHM using two-component error model, separate fits updated assuming two-component error. Using two-component error model, one fit converge constant variance converge, non-SFO fits failed converge.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\") f_sep_const <- mmkin(   deg_mods,   dmta_ds,   error_model = \"const\",   quiet = TRUE)  status(f_sep_const) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hierarchichal-model-fits","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchichal model fits","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"following code fits eight versions hierarchical models data, using SFO, FOMC, DFOP HS parent compound, using either constant variance two-component error error model. default parameter distribution model mkin allows variation degradation parameters across assumed population soils. words, degradation parameter associated random effect first step. mhmkin function makes possible fit eight versions parallel (given sufficient number computing cores available) save execution time. Convergence plots summaries fits shown appendix. output status function shows fits terminated successfully. AIC BIC values show biphasic models DFOP HS give best fits. DFOP model preferred , better mechanistic basis batch experiments constant incubation conditions. Also, shows lowest AIC BIC values first set fits combined two-component error model. Therefore, DFOP model selected refinements fits aim make model fully identifiable.","code":"f_saem <- mhmkin(list(f_sep_const, f_sep_tc), transformations = \"saemix\") status(f_saem) |> kable() anova(f_saem) |> kable(digits = 1)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"parameter-identifiability-based-on-the-fisher-information-matrix","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Parameter identifiability based on the Fisher Information Matrix","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Using illparms function, ill-defined statistical model parameters standard deviations degradation parameters population error model parameters can found. According illparms function, fitted standard deviation second kinetic rate constant k2 ill-defined DFOP fits. suggests different values obtained standard deviation using different starting values. thus identified overparameterisation addressed removing random effect k2 parameter model. resulting fit, checked whether still ill-defined parameters, case. , refined model compared previous best model. model without random effect k2 reduced version previous model. Therefore, models nested can compared using likelihood ratio test. achieved argument test = TRUE anova function. AIC BIC criteria lower removal ill-defined random effect k2. p value likelihood ratio test much greater 0.05, indicating model higher likelihood (model random effects degradation parameters f_saem[[\"DFOP\", \"tc\"]]) fit significantly better model lower likelihood (reduced model f_saem_dfop_tc_no_ranef_k2). Therefore, AIC, BIC likelihood ratio test suggest use reduced model. convergence fit checked visually. Convergence plot NLHM DFOP fit two-component error without random effect ‘k2’ parameters appear converged satisfactory degree. final fit plotted using plot method mkin package. Plot final NLHM DFOP fit Finally, summary report fit produced.","code":"illparms(f_saem) |> kable() f_saem_dfop_tc_no_ranef_k2 <- update(f_saem[[\"DFOP\", \"tc\"]],   no_random_effect = \"k2\") illparms(f_saem_dfop_tc_no_ranef_k2) anova(f_saem[[\"DFOP\", \"tc\"]], f_saem_dfop_tc_no_ranef_k2, test = TRUE) |>   kable(format.args = list(digits = 4)) plot(f_saem_dfop_tc_no_ranef_k2) summary(f_saem_dfop_tc_no_ranef_k2) saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:46 2025  Date of summary: Mon May 12 21:06:46 2025   Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 3.953 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0       k1       k2        g  98.71186  0.08675  0.01374  0.93491   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 g DMTA_0  98.71  0  0 0 k1       0.00  1  0 0 k2       0.00  0  1 0 g        0.00  0  0 1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   663.7 661.8 -322.9  Optimised parameters:                est.     lower     upper DMTA_0    98.256267 96.286112 100.22642 k1         0.064037  0.033281   0.09479 k2         0.008469  0.006002   0.01094 g          0.954167  0.914460   0.99387 a.1        1.061795  0.878608   1.24498 b.1        0.029550  0.022593   0.03651 SD.DMTA_0  2.068581  0.427178   3.70998 SD.k1      0.598285  0.258235   0.93833 SD.g       1.016689  0.360061   1.67332  Correlation:     DMTA_0  k1      k2      k1  0.0213                 k2  0.0541  0.0344         g  -0.0521 -0.0286 -0.2744  Random effects:             est.  lower  upper SD.DMTA_0 2.0686 0.4272 3.7100 SD.k1     0.5983 0.2582 0.9383 SD.g      1.0167 0.3601 1.6733  Variance model:        est.   lower   upper a.1 1.06180 0.87861 1.24498 b.1 0.02955 0.02259 0.03651  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 11.45 41.32    12.44   10.82   81.85"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"alternative-check-of-parameter-identifiability","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Alternative check of parameter identifiability","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"parameter check used illparms function based quadratic approximation likelihood surface near optimum, calculated using Fisher Information Matrix (FIM). alternative way check parameter identifiability (Duchesne et al. 2021) based multistart approach recently implemented mkin. graph shows boxplots parameters obtained 50 runs saem algorithm different parameter combinations, sampled range parameters obtained individual datasets fitted separately using nonlinear regression. Scaled parameters multistart runs, full model graph clearly confirms lack identifiability variance k2 full model. overparameterisation model also indicates lack identifiability variance parameter g. parameter boxplots multistart runs reduced model shown indicate runs give similar results, regardless starting parameters. Scaled parameters multistart runs, reduced model parameters top 25% fits shown (based feature introduced mkin 1.2.2 currently development), scatter even less shown . Scaled parameters multistart runs, reduced model, fits top 25% likelihood values","code":"f_saem_dfop_tc_multi <- multistart(f_saem[[\"DFOP\", \"tc\"]], n = 50, cores = 15) par(mar = c(6.1, 4.1, 2.1, 2.1)) parplot(f_saem_dfop_tc_multi, lpos = \"bottomright\", ylim = c(0.3, 10), las = 2) f_saem_dfop_tc_no_ranef_k2_multi <- multistart(f_saem_dfop_tc_no_ranef_k2,   n = 50, cores = 15) par(mar = c(6.1, 4.1, 2.1, 2.1)) parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2,   lpos = \"bottomright\") par(mar = c(6.1, 4.1, 2.1, 2.1)) parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2, llquant = 0.25,   lpos = \"bottomright\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"conclusions","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusions","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Fitting four parent degradation models SFO, FOMC, DFOP HS part hierarchical model fits two different error models normal distributions transformed degradation parameters works without technical problems. biphasic models DFOP HS gave best fit data, default parameter distribution model fully identifiable. Removing random effect second kinetic rate constant DFOP model resulted reduced model fully identifiable showed lowest values model selection criteria AIC BIC. reliability identification model parameters confirmed using multiple starting values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"acknowledgements","dir":"Articles > Prebuilt","previous_headings":"","what":"Acknowledgements","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"helpful comments Janina Wöltjen German Environment Agency gratefully acknowledged.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hierarchical-model-fit-listings","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hierarchical model fit listings","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Hierarchical mkin fit SFO model error model const  Hierarchical mkin fit SFO model error model tc  Hierarchical mkin fit FOMC model error model const  Hierarchical mkin fit FOMC model error model tc  Hierarchical mkin fit DFOP model error model const  Hierarchical mkin fit DFOP model error model tc  Hierarchical mkin fit HS model error model const  Hierarchical mkin fit HS model error model tc","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:38 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - k_DMTA * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 0.793 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:  DMTA_0  k_DMTA  97.2953  0.0566   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k_DMTA DMTA_0   97.3      0 k_DMTA    0.0      1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   796.3 795.3 -393.2  Optimised parameters:               est.    lower   upper DMTA_0    97.28130 95.71113 98.8515 k_DMTA     0.05665  0.02909  0.0842 a.1        2.66442  2.35579  2.9731 SD.DMTA_0  1.54776  0.15447  2.9411 SD.k_DMTA  0.60690  0.26248  0.9513  Correlation:         DMTA_0 k_DMTA 0.0168  Random effects:             est.  lower  upper SD.DMTA_0 1.5478 0.1545 2.9411 SD.k_DMTA 0.6069 0.2625 0.9513  Variance model:      est. lower upper a.1 2.664 2.356 2.973  Estimated disappearance times:       DT50  DT90 DMTA 12.24 40.65 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:40 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - k_DMTA * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 2.462 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0   k_DMTA  96.99175  0.05603   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k_DMTA DMTA_0  96.99      0 k_DMTA   0.00      1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   798.3 797.1 -393.2  Optimised parameters:                est.    lower    upper DMTA_0    97.271822 95.70316 98.84049 k_DMTA     0.056638  0.02911  0.08417 a.1        2.660081  2.27492  3.04525 b.1        0.001665 -0.14451  0.14784 SD.DMTA_0  1.545520  0.14301  2.94803 SD.k_DMTA  0.606422  0.26227  0.95057  Correlation:         DMTA_0 k_DMTA 0.0169  Random effects:             est.  lower  upper SD.DMTA_0 1.5455 0.1430 2.9480 SD.k_DMTA 0.6064 0.2623 0.9506  Variance model:         est.   lower  upper a.1 2.660081  2.2749 3.0452 b.1 0.001665 -0.1445 0.1478  Estimated disappearance times:       DT50  DT90 DMTA 12.24 40.65 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:39 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 1.408 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:  DMTA_0   alpha    beta   98.292   9.909 156.341   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 alpha beta DMTA_0  98.29     0    0 alpha    0.00     1    0 beta     0.00     0    1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   734.2 732.7 -360.1  Optimised parameters:               est.   lower   upper DMTA_0     98.3435 96.9033  99.784 alpha       7.2007  2.5889  11.812 beta      112.8745 34.8816 190.867 a.1         2.0459  1.8054   2.286 SD.DMTA_0   1.4795  0.2717   2.687 SD.alpha    0.6396  0.1509   1.128 SD.beta     0.6874  0.1587   1.216  Correlation:        DMTA_0  alpha   alpha -0.1125         beta  -0.1227  0.3632  Random effects:             est.  lower upper SD.DMTA_0 1.4795 0.2717 2.687 SD.alpha  0.6396 0.1509 1.128 SD.beta   0.6874 0.1587 1.216  Variance model:      est. lower upper a.1 2.046 1.805 2.286  Estimated disappearance times:       DT50  DT90 DT50back DMTA 11.41 42.53     12.8 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:40 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 2.735 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters: DMTA_0  alpha   beta  98.772  4.663 92.597   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 alpha beta DMTA_0  98.77     0    0 alpha    0.00     1    0 beta     0.00     0    1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   720.7 719.1 -352.4  Optimised parameters:               est.    lower     upper DMTA_0    99.10577 97.33296 100.87859 alpha      5.46260  2.52199   8.40321 beta      81.66080 30.46664 132.85497 a.1        1.50219  1.25801   1.74636 b.1        0.02893  0.02048   0.03739 SD.DMTA_0  1.61887 -0.03843   3.27618 SD.alpha   0.58145  0.17364   0.98925 SD.beta    0.68205  0.21108   1.15302  Correlation:        DMTA_0  alpha   alpha -0.1321         beta  -0.1430  0.2467  Random effects:             est.    lower  upper SD.DMTA_0 1.6189 -0.03843 3.2762 SD.alpha  0.5814  0.17364 0.9892 SD.beta   0.6821  0.21108 1.1530  Variance model:        est.   lower   upper a.1 1.50219 1.25801 1.74636 b.1 0.02893 0.02048 0.03739  Estimated disappearance times:       DT50  DT90 DT50back DMTA 11.05 42.81    12.89 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:39 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 1.769 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:   DMTA_0       k1       k2        g  98.64383  0.09211  0.02999  0.76814   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 g DMTA_0  98.64  0  0 0 k1       0.00  1  0 0 k2       0.00  0  1 0 g        0.00  0  0 1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC BIC logLik   711.8 710 -346.9  Optimised parameters:                est.     lower    upper DMTA_0    98.092481 96.573899 99.61106 k1         0.062499  0.030336  0.09466 k2         0.009065 -0.005133  0.02326 g          0.948967  0.862080  1.03586 a.1        1.821671  1.604774  2.03857 SD.DMTA_0  1.677785  0.472066  2.88350 SD.k1      0.634962  0.270788  0.99914 SD.k2      1.033498 -0.205994  2.27299 SD.g       1.710046  0.428642  2.99145  Correlation:     DMTA_0  k1      k2      k1  0.0246                 k2  0.0491  0.0953         g  -0.0552 -0.0889 -0.4795  Random effects:            est.   lower  upper SD.DMTA_0 1.678  0.4721 2.8835 SD.k1     0.635  0.2708 0.9991 SD.k2     1.033 -0.2060 2.2730 SD.g      1.710  0.4286 2.9914  Variance model:      est. lower upper a.1 1.822 1.605 2.039  Estimated disappearance times:       DT50 DT90 DT50back DT50_k1 DT50_k2 DMTA 11.79 42.8    12.88   11.09   76.46 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:41 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 3.069 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0       k1       k2        g  98.71186  0.08675  0.01374  0.93491   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 g DMTA_0  98.71  0  0 0 k1       0.00  1  0 0 k2       0.00  0  1 0 g        0.00  0  0 1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   665.7 663.6 -322.9  Optimised parameters:                est.     lower     upper DMTA_0    98.347470 96.380815 100.31413 k1         0.064524  0.034279   0.09477 k2         0.008304  0.005843   0.01076 g          0.952128  0.909578   0.99468 a.1        1.068907  0.883665   1.25415 b.1        0.029265  0.022318   0.03621 SD.DMTA_0  2.065796  0.427951   3.70364 SD.k1      0.583703  0.251796   0.91561 SD.k2      0.004167 -7.831228   7.83956 SD.g       1.064450  0.397479   1.73142  Correlation:     DMTA_0  k1      k2      k1  0.0223                 k2  0.0568  0.0394         g  -0.0464 -0.0269 -0.2713  Random effects:               est.   lower  upper SD.DMTA_0 2.065796  0.4280 3.7036 SD.k1     0.583703  0.2518 0.9156 SD.k2     0.004167 -7.8312 7.8396 SD.g      1.064450  0.3975 1.7314  Variance model:        est.   lower   upper a.1 1.06891 0.88367 1.25415 b.1 0.02927 0.02232 0.03621  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 11.39 41.36    12.45   10.74   83.48 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:39 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 1.996 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:   DMTA_0       k1       k2       tb  97.82176  0.06931  0.02997 11.13945   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 tb DMTA_0  97.82  0  0  0 k1       0.00  1  0  0 k2       0.00  0  1  0 tb       0.00  0  0  1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling   AIC   BIC logLik   714 712.1   -348  Optimised parameters:               est.    lower    upper DMTA_0    98.16102 96.47747 99.84456 k1         0.07876  0.05261  0.10491 k2         0.02227  0.01706  0.02747 tb        13.99089 -7.40049 35.38228 a.1        1.82305  1.60700  2.03910 SD.DMTA_0  1.88413  0.56204  3.20622 SD.k1      0.34292  0.10482  0.58102 SD.k2      0.19851  0.01718  0.37985 SD.tb      1.68168  0.58064  2.78272  Correlation:     DMTA_0  k1      k2      k1  0.0142                 k2  0.0001 -0.0025         tb  0.0165 -0.1256 -0.0301  Random effects:             est.   lower  upper SD.DMTA_0 1.8841 0.56204 3.2062 SD.k1     0.3429 0.10482 0.5810 SD.k2     0.1985 0.01718 0.3798 SD.tb     1.6817 0.58064 2.7827  Variance model:      est. lower upper a.1 1.823 1.607 2.039  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 8.801 67.91    20.44   8.801   31.13 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:41 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 3.389 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0       k1       k2       tb  98.45190  0.07525  0.02576 19.19375   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 tb DMTA_0  98.45  0  0  0 k1       0.00  1  0  0 k2       0.00  0  1  0 tb       0.00  0  0  1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC BIC logLik   667.1 665 -323.6  Optimised parameters:               est.    lower    upper DMTA_0    97.76571 95.81350 99.71791 k1         0.05855  0.03080  0.08630 k2         0.02337  0.01664  0.03010 tb        31.09638 29.38289 32.80987 a.1        1.08835  0.90059  1.27611 b.1        0.02964  0.02261  0.03667 SD.DMTA_0  2.04877  0.42553  3.67200 SD.k1      0.59166  0.25621  0.92711 SD.k2      0.30698  0.09561  0.51835 SD.tb      0.01274 -0.10915  0.13464  Correlation:     DMTA_0  k1      k2      k1  0.0160                 k2 -0.0070 -0.0024         tb -0.0668 -0.0103 -0.2013  Random effects:              est.    lower  upper SD.DMTA_0 2.04877  0.42553 3.6720 SD.k1     0.59166  0.25621 0.9271 SD.k2     0.30698  0.09561 0.5183 SD.tb     0.01274 -0.10915 0.1346  Variance model:        est.   lower   upper a.1 1.08835 0.90059 1.27611 b.1 0.02964 0.02261 0.03667  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 11.84 51.71    15.57   11.84   29.66"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hierarchical-model-convergence-plots","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hierarchical model convergence plots","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Convergence plot NLHM SFO fit constant variance Convergence plot NLHM SFO fit two-component error Convergence plot NLHM FOMC fit constant variance Convergence plot NLHM FOMC fit two-component error Convergence plot NLHM DFOP fit constant variance Convergence plot NLHM DFOP fit two-component error Convergence plot NLHM HS fit constant variance Convergence plot NLHM HS fit two-component error","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  gridExtra_2.3     jquerylib_0.1.4   systemfonts_1.2.1  [9] scales_1.3.0      textshaping_1.0.0 yaml_2.3.10       fastmap_1.2.0     [13] lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          generics_0.1.3    [17] lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 tibble_3.2.1      [21] desc_1.4.3        munsell_0.5.1     bslib_0.9.0       pillar_1.10.1     [25] rlang_1.1.5       cachem_1.1.0      xfun_0.51         fs_1.6.5          [29] sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     magrittr_2.0.3    [33] digest_0.6.37     grid_4.5.0        mclust_6.1.1      lifecycle_1.0.4   [37] nlme_3.1-168      vctrs_0.6.5       evaluate_1.0.3    glue_1.8.0        [41] codetools_0.2-20  ragg_1.3.3        zoo_1.8-13        colorspace_2.1-1  [45] tools_4.5.0       pkgconfig_2.0.3   htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS, parallel formation two metabolites can fitted mkin package. assembled course work package 1.2 Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) German Environment Agency carried 2022 2023. mkin package used version 1.2.10, currently development. contains test data, functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores()  # We need to start a new cluster after defining a compiled model that is # saved as a DLL to the user directory, therefore we define a function # This is used again after defining the pathway model start_cluster <- function(n_cores) {   if (Sys.info()[\"sysname\"] == \"Windows\") {     ret <- makePSOCKcluster(n_cores)   } else {     ret <- makeForkCluster(n_cores)   }   return(ret) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"data","dir":"Articles > Prebuilt","previous_headings":"","what":"Data","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"test data available mkin package object class mkindsg (mkin dataset group) identifier dimethenamid_2018. following preprocessing steps done document. data available enantiomer dimethenamid-P (DMTAP) renamed substance name data racemic mixture dimethenamid (DMTA). reason difference degradation behaviour identified EU risk assessment. Unnecessary columns discarded observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters independent temperature Finally, datasets observed soil (Elliot 1 Elliot 2) combined, resulting dimethenamid (DMTA) data six soils. following commented R code performs preprocessing. following tables show 6 datasets. Dataset Calke Dataset Borstel Dataset Flaach Dataset BBA 2.2 Dataset BBA 2.3 Dataset Elliot","code":"# Apply a function to each of the seven datasets in the mkindsg object to create a list dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data                     # Get a dataset   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"              # Rename DMTAP to DMTA   ds_i <- subset(ds_i, select = c(\"name\", \"time\", \"value\")) # Select data   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]  # Normalise time   ds_i                                                       # Return the dataset })  # Use dataset titles as names for the list elements names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)  # Combine data for Elliot soil to obtain a named list with six elements dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) # dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL for (ds_name in names(dmta_ds)) {   print(     kable(mkin_long_to_wide(dmta_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE))     cat(\"\\n\\\\clearpage\\n\") }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"separate-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Separate evaluations","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"first step obtain suitable starting parameters NLHM fits, separate fits several variants pathway model used previously (Ranke et al. 2021), varying kinetic model parent compound. SFORB model often provides faster convergence DFOP model, can sometimes fitted DFOP model results errors, included set parent models tested . separate pathway fits SFO FOMC parent constant variance converged (status OK). fits DFOP SFORB parent converged well. fits HS parent converge default settings. two-component error model, set fits convergence problems slightly different, convergence problems appearing different data sets applying DFOP SFORB model additional convergence problems using FOMC model parent.","code":"if (!dir.exists(\"dmta_dlls\")) dir.create(\"dmta_dlls\") m_sfo_path_1 <- mkinmod(   DMTA = mkinsub(\"SFO\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_sfo_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_fomc_path_1 <- mkinmod(   DMTA = mkinsub(\"FOMC\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_fomc_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_dfop_path_1 <- mkinmod(   DMTA = mkinsub(\"DFOP\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_dfop_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_sforb_path_1 <- mkinmod(   DMTA = mkinsub(\"SFORB\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_sforb_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_hs_path_1 <- mkinmod(   DMTA = mkinsub(\"HS\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_hs_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) cl <- start_cluster(n_cores)  deg_mods_1 <- list(   sfo_path_1 = m_sfo_path_1,   fomc_path_1 = m_fomc_path_1,   dfop_path_1 = m_dfop_path_1,   sforb_path_1 = m_sforb_path_1,   hs_path_1 = m_hs_path_1)  sep_1_const <- mmkin(   deg_mods_1,   dmta_ds,   error_model = \"const\",   quiet = TRUE)  status(sep_1_const) |> kable() sep_1_tc <- update(sep_1_const, error_model = \"tc\") status(sep_1_tc) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"hierarchichal-model-fits","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchichal model fits","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"following code fits two sets corresponding hierarchical models data, one assuming constant variance, one assuming two-component error. run time fits around two hours five year old hardware. recent hardware upgrade fits complete less twenty minutes. According status function, fits terminated successfully. goodness--fit models compared, warning obtained, indicating likelihood pathway fit SFORB parent compound constant variance calculated importance sampling (method ‘’). default method AIC BIC comparisons based, variant included model comparison table. Comparing goodness--fit remaining models, HS model model two-component error provides best fit. However, batch experiments performed constant conditions experiments evaluated , reason assume discontinuity, SFORB model preferable mechanistic viewpoint. addition, information criteria AIC BIC similar HS SFORB. Therefore, SFORB model selected refinements.","code":"saem_1 <- mhmkin(list(sep_1_const, sep_1_tc)) status(saem_1) |> kable() anova(saem_1) |> kable(digits = 1)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"parameter-identifiability-based-on-the-fisher-information-matrix","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Parameter identifiability based on the Fisher Information Matrix","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"Using illparms function, ill-defined statistical model parameters standard deviations degradation parameters population error model parameters can found. using constant variance, ill-defined variance parameters identified illparms function degradation models. using two-component error model, one ill-defined variance parameter variants except variant using DFOP parent compound. selected combination SFORB pathway model two-component error, random effect rate constant reversibly bound DMTA free DMTA (k_DMTA_bound_free) well-defined. Therefore, fit updated without assuming random effect parameter. expected, ill-defined parameters remain. model comparison shows reduced model preferable. convergence plot refined fit shown .  parameters, example f_DMTA_ilr_1 f_DMTA_ilr_2, .e. two parameters determining formation fractions parallel formation three metabolites, movement parameters still visible second phase algorithm. However, amplitude movement range amplitude towards end first phase. Therefore, likely increase iterations improve parameter estimates much, proposed fit acceptable. numeric convergence criterion implemented saemix.","code":"illparms(saem_1) |> kable() saem_sforb_path_1_tc_reduced <- update(saem_1[[\"sforb_path_1\", \"tc\"]],   no_random_effect = \"log_k_DMTA_bound_free\") illparms(saem_sforb_path_1_tc_reduced) anova(saem_1[[\"sforb_path_1\", \"tc\"]], saem_sforb_path_1_tc_reduced) |> kable(digits = 1) plot(saem_sforb_path_1_tc_reduced$so, plot.type = \"convergence\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"alternative-check-of-parameter-identifiability","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Alternative check of parameter identifiability","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"alternative check parameter identifiability (Duchesne et al. 2021), multistart runs performed basis refined fit shown . 32 fits initiated, 17 terminated without error. reason wide variation starting parameters combination parameter variation used SAEM algorithm leads parameter combinations degradation model numerical integration routine cope . variation initial parameters, model fits take two times time original fit. Parameter boxplots multistart runs succeeded However, visual analysis boxplot parameters obtained successful fits confirms results sufficiently independent starting parameters, remaining ill-defined parameters.","code":"saem_sforb_path_1_tc_reduced_multi <- multistart(saem_sforb_path_1_tc_reduced,   n = 32, cores = 10) (subscript) logical subscript too long print(saem_sforb_path_1_tc_reduced_multi) <multistart> object with 32 fits:  E OK   7 25  OK: Fit terminated successfully E: Error par(mar = c(12.1, 4.1, 2.1, 2.1)) parplot(saem_sforb_path_1_tc_reduced_multi, ylim = c(0.5, 2), las = 2)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"plots-of-selected-fits","dir":"Articles > Prebuilt","previous_headings":"","what":"Plots of selected fits","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"SFORB pathway fits full reduced parameter distribution model shown . SFORB pathway fit two-component error SFORB pathway fit two-component error, reduced parameter model Plots remaining fits listings successful fits shown Appendix.","code":"plot(saem_1[[\"sforb_path_1\", \"tc\"]]) plot(saem_sforb_path_1_tc_reduced) stopCluster(cl)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"conclusions","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusions","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"Pathway fits SFO, FOMC, DFOP, SFORB HS models parent compound successfully performed.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"acknowledgements","dir":"Articles > Prebuilt","previous_headings":"","what":"Acknowledgements","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"helpful comments Janina Wöltjen German Environment Agency earlier versions document gratefully acknowledged.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"plots-of-hierarchical-fits-not-selected-for-refinement","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Plots of hierarchical fits not selected for refinement","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"SFO pathway fit two-component error FOMC pathway fit two-component error HS pathway fit two-component error","code":"plot(saem_1[[\"sfo_path_1\", \"tc\"]]) plot(saem_1[[\"fomc_path_1\", \"tc\"]]) plot(saem_1[[\"sforb_path_1\", \"tc\"]])"},{"path":[]},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] sass_0.4.9        generics_0.1.3    lattice_0.22-6    digest_0.6.37      [5] magrittr_2.0.3    evaluate_1.0.3    grid_4.5.0        fastmap_1.2.0      [9] jsonlite_1.9.0    processx_3.8.6    pkgbuild_1.4.6    deSolve_1.40      [13] mclust_6.1.1      ps_1.9.0          gridExtra_2.3     scales_1.3.0      [17] codetools_0.2-20  textshaping_1.0.0 jquerylib_0.1.4   cli_3.6.4         [21] rlang_1.1.5       munsell_0.5.1     cachem_1.1.0      yaml_2.3.10       [25] tools_4.5.0       inline_0.3.21     dplyr_1.1.4       colorspace_2.1-1  [29] ggplot2_3.5.1     vctrs_0.6.5       R6_2.6.1          zoo_1.8-13        [33] lifecycle_1.0.4   fs_1.6.5          htmlwidgets_1.6.4 MASS_7.3-65       [37] ragg_1.3.3        pkgconfig_2.0.3   desc_1.4.3        callr_3.7.6       [41] pkgdown_2.1.1     pillar_1.10.1     bslib_0.9.0       gtable_0.3.6      [45] glue_1.8.0        systemfonts_1.2.1 xfun_0.51         tibble_3.2.1      [49] lmtest_0.9-40     tidyselect_1.2.1  htmltools_0.5.8.1 nlme_3.1-168      [53] compiler_4.5.0"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package, also considering influence covariates like soil pH different degradation parameters. case studies, SFORB parameterisation biexponential decline shown advantages DFOP parameterisation, SFORB included list tested models well. mkin package used version 1.2.10, contains functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"test-data","dir":"Articles > Prebuilt","previous_headings":"Introduction","what":"Test data","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following tables show covariate data 18 datasets read spreadsheet file. Covariate data Dataset Richmond Dataset Richmond 2 Dataset ERTC Dataset Toulouse Dataset Picket Piece Dataset 721 Dataset 722 Dataset 723 Dataset 724 Dataset 725 Dataset 727 Dataset 728 Dataset 729 Dataset 730 Dataset 731 Dataset 732 Dataset 741 Dataset 742","code":"data_file <- system.file(   \"testdata\", \"mesotrione_soil_efsa_2016.xlsx\", package = \"mkin\") meso_ds <- read_spreadsheet(data_file, parent_only = TRUE) pH <- attr(meso_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(meso_ds)) {   print(     kable(mkin_long_to_wide(meso_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE)) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"separate-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Separate evaluations","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"order obtain suitable starting parameters NLHM fits, separate fits five models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. tables , OK indicates convergence C indicates failure converge. separate fits constant variance converged, exception two FOMC fits, one SFORB fit one HS fit. two-component error model, set fits converge larger, convergence problems appearing number non-SFO fits.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\") f_sep_const <- mmkin(   deg_mods,   meso_ds,   error_model = \"const\",   cluster = cl,   quiet = TRUE) status(f_sep_const[, 1:5]) |> kable() status(f_sep_const[, 6:18]) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc[, 1:5]) |> kable() status(f_sep_tc[, 6:18]) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hierarchical-model-fits-without-covariate-effect","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchical model fits without covariate effect","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following code fits hierarchical kinetic models ten combinations five different degradation models two different error models parallel. fits terminate without errors (status OK). model comparisons show fits constant variance consistently preferable corresponding fits two-component error data. confirmed fact parameter b.1 (relative standard deviation fits obtained saemix package), ill-defined fits. obtaining fits well-defined random effects, update set fits, excluding random effects ill-defined according illparms function. updated fits terminate without errors. ill-defined errors remain fits constant variance.","code":"f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) status(f_saem_1) |> kable() anova(f_saem_1) |> kable(digits = 1) illparms(f_saem_1) |> kable() f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hierarchical-model-fits-with-covariate-effect","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchical model fits with covariate effect","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following sections, hierarchical fits including model influence pH selected degradation parameters shown parent models. Constant variance selected error model based fits without covariate effects. Random effects ill-defined fits without pH influence excluded. potential influence soil pH included parameters well-defined random effect, experience shown parameters significant pH effect found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"sfo","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"SFO","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"parameter showing pH influence table beta_pH(log_k_meso). confidence interval include zero, indicating influence soil pH log degradation rate constant significantly greater zero. comparison SFO fit without covariate effect confirms considering soil pH improves model, comparison AIC BIC likelihood ratio test.  Endpoints model covariates default calculated median covariate values. quantile can adapted, specific covariate value can given shown .","code":"sfo_pH <- saem(f_sep_const[\"SFO\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_k_meso ~ pH)) summary(sfo_pH)$confint_trans |> kable(digits = 2) anova(f_saem_2[[\"SFO\", \"const\"]], sfo_pH, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"SFO\", \"const\"]]    4 797.56 801.12 -394.78                          sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(sfo_pH) endpoints(sfo_pH) $covariates       pH 50% 5.75  $distimes          DT50     DT90 meso 18.52069 61.52441 endpoints(sfo_pH, covariate_quantile = 0.9) $covariates       pH 90% 7.13  $distimes          DT50     DT90 meso 8.237019 27.36278 endpoints(sfo_pH, covariates = c(pH = 7.0)) $covariates      pH User  7  $distimes         DT50    DT90 meso 8.89035 29.5331"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"fomc","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"FOMC","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"case SFO, confidence interval slope parameter (beta_pH(log_alpha)) quantifying influence soil pH include zero, model comparison clearly indicates model covariate influence preferable. However, random effect alpha well-defined inclusion covariate effect (confidence interval SD.log_alpha includes zero). Therefore, model updated without random effect, ill-defined parameters remain. Model comparison indicates including pH dependence significantly improves fit, reduced model covariate influence results preferable FOMC fit.","code":"fomc_pH <- saem(f_sep_const[\"FOMC\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_alpha ~ pH)) summary(fomc_pH)$confint_trans |> kable(digits = 2) illparms(fomc_pH) [1] \"sd(log_alpha)\" fomc_pH_2 <- update(fomc_pH, no_random_effect = c(\"meso_0\", \"log_alpha\")) illparms(fomc_pH_2) anova(f_saem_2[[\"FOMC\", \"const\"]], fomc_pH, fomc_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"FOMC\", \"const\"]]    5 783.25 787.71 -386.63                          fomc_pH_2                      6 767.49 772.83 -377.75 17.762  1  2.503e-05 *** fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(fomc_pH_2)$confint_trans |> kable(digits = 2) plot(fomc_pH_2) endpoints(fomc_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back meso 17.30248 82.91343 24.95943 endpoints(fomc_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back meso 6.986239 27.02927 8.136621"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"dfop","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"DFOP","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"DFOP fits without covariate effects, random effects two degradation parameters (k2 g) identifiable. fit pH dependent degradation parameters obtained excluding random effects refined DFOP fit without covariate influence, including covariate models two identifiable parameters k2 g. corresponding parameters influence soil pH beta_pH(log_k2) influence soil pH k2, beta_pH(g_qlogis) influence g. Confidence intervals neither include zero, indicating significant difference zero. However, random effect g now ill-defined. fit updated without ill-defined random effect. Now, slope parameter pH effect g ill-defined. Therefore, another attempt made without corresponding covariate model. random effect g ill-defined, fit repeated without . ill-defined parameters remain, model comparison suggests previous model dfop_pH_2 two pH dependent parameters preferable, based information criteria well based likelihood ratio test. focussing parameter identifiability using test confidence interval includes zero, dfop_pH_4 still preferred model. However, kept mind parameter confidence intervals constructed using simple linearisation likelihood. confidence interval random effect g marginally includes zero, suggested acceptable, dfop_pH_2 can considered preferable model.","code":"summary(f_saem_2[[\"DFOP\", \"const\"]])$confint_trans |> kable(digits = 2) dfop_pH <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH)) summary(dfop_pH)$confint_trans |> kable(digits = 2) illparms(dfop_pH) [1] \"sd(g_qlogis)\" dfop_pH_2 <- update(dfop_pH,   no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_2) [1] \"beta_pH(g_qlogis)\" dfop_pH_3 <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH)) illparms(dfop_pH_3) [1] \"sd(g_qlogis)\" dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_4) anova(f_saem_2[[\"DFOP\", \"const\"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik f_saem_2[[\"DFOP\", \"const\"]]    7 782.94 789.18 -384.47 dfop_pH_4                      7 767.35 773.58 -376.68 dfop_pH_2                      8 765.14 772.26 -374.57 dfop_pH_3                      8 769.00 776.12 -376.50 dfop_pH                        9 769.10 777.11 -375.55 anova(dfop_pH_2, dfop_pH_4, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)   dfop_pH_4    7 767.35 773.58 -376.68                        dfop_pH_2    8 765.14 772.26 -374.57 4.2153  1    0.04006 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(dfop_pH_2) endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"sforb","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"SFORB","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"confidence interval beta_pH(log_k_meso_bound_free) includes zero, indicating influence soil pH k_meso_bound_free reliably quantified. Also, confidence interval random effect parameter (SD.log_k_meso_bound_free) includes zero. Using illparms function, ill-defined parameters can found conveniently. remove ill-defined parameters, second variant SFORB model pH influence fitted. ill-defined parameters remain. model comparison SFORB fits includes refined model without covariate effect, versions SFORB fit covariate effect. first model including pH influence preferable based information criteria likelihood ratio test. However, fully identifiable, second model selected.","code":"sforb_pH <- saem(f_sep_const[\"SFORB\", ], no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\"),   covariates = pH,   covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH)) summary(sforb_pH)$confint_trans |> kable(digits = 2) illparms(sforb_pH) [1] \"sd(log_k_meso_bound_free)\"      \"beta_pH(log_k_meso_bound_free)\" sforb_pH_2 <- update(sforb_pH,   no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\", \"log_k_meso_bound_free\"),   covariate_models = list(log_k_meso_free ~ pH)) illparms(sforb_pH_2) anova(f_saem_2[[\"SFORB\", \"const\"]], sforb_pH, sforb_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                               npar    AIC    BIC     Lik   Chisq Df Pr(>Chisq)   f_saem_2[[\"SFORB\", \"const\"]]    7 783.40 789.63 -384.70                         sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0              sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(sforb_pH_2)$confint_trans |> kable(digits = 2) plot(sforb_pH_2) endpoints(sforb_pH_2) $covariates       pH 50% 5.75  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.09735824 0.02631699 0.31602120   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.86549 73.15824 22.02282     7.119554     26.33839 endpoints(sforb_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.13315233 0.03795988 0.61186191   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 7.932495 36.93311 11.11797     5.205671        18.26"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hs","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"HS","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"According output illparms function, random effect break time tb reliably quantified, neither can influence soil pH tb. fit repeated without corresponding covariate model, ill-defined parameters remain. Model comparison confirms model preferable fit without covariate influence, also first version covariate influence.","code":"hs_pH <- saem(f_sep_const[\"HS\", ], no_random_effect = c(\"meso_0\"),   covariates = pH,   covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH)) summary(hs_pH)$confint_trans |> kable(digits = 2) illparms(hs_pH) [1] \"sd(log_tb)\"      \"beta_pH(log_tb)\" hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) illparms(hs_pH_2) anova(f_saem_2[[\"HS\", \"const\"]], hs_pH, hs_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"HS\", \"const\"]]    8 780.08 787.20 -382.04                          hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 *** hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(hs_pH_2)$confint_trans |> kable(digits = 2) plot(hs_pH_2) endpoints(hs_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 14.68725 82.45287 24.82079 14.68725 29.29299 endpoints(hs_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.298536 38.85371 11.69613 8.298536 15.71561"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"comparison-across-parent-models","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"Comparison across parent models","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"model reduction models pH influence, compared . DFOP model pH influence k2 g random effect k2 finally selected best fit. endpoints resulting model listed . Please refer Appendix detailed listing.","code":"anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2) Data: 116 observations of 1 variable(s) grouped in 18 datasets             npar    AIC    BIC     Lik sfo_pH        5 783.09 787.54 -386.54 fomc_pH_2     6 767.49 772.83 -377.75 dfop_pH_4     7 767.35 773.58 -376.68 sforb_pH_2    7 770.94 777.17 -378.47 dfop_pH_2     8 765.14 772.26 -374.57 hs_pH_2      10 766.47 775.37 -373.23 endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"conclusions","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusions","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"evaluations demonstrate covariate effects can included types parent degradation models. models can refined make fully identifiable.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  gridExtra_2.3     jquerylib_0.1.4   systemfonts_1.2.1  [9] scales_1.3.0      textshaping_1.0.0 readxl_1.4.4      yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.9.0       [25] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51         [29] fs_1.6.5          sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     [33] magrittr_2.0.3    digest_0.6.37     grid_4.5.0        mclust_6.1.1      [37] lifecycle_1.0.4   nlme_3.1-168      vctrs_0.6.5       evaluate_1.0.3    [41] glue_1.8.0        cellranger_1.1.0  codetools_0.2-20  ragg_1.3.3        [45] zoo_1.8-13        colorspace_2.1-1  tools_4.5.0       pkgconfig_2.0.3   [49] htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"the-data","dir":"Articles > Web_only","previous_headings":"","what":"The data","title":"Example evaluation of FOCUS dataset Z","text":"following code defines example dataset Appendix 7 FOCUS kinetics report (FOCUS Work Group Degradation Kinetics 2014, 354).","code":"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,         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,          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,          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,          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,          25.2, 17.2, 4.8, 4.5, 2.8, 4.4))  FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"parent-and-one-metabolite","dir":"Articles > Web_only","previous_headings":"","what":"Parent and one metabolite","title":"Example evaluation of FOCUS dataset Z","text":"next step set models used kinetic analysis. simultaneous fit parent first metabolite usually straightforward, Step 1 (SFO parent ) skipped . start model 2a, formation decline metabolite Z1 pathway parent directly sink included (default mkin).  obvious parameter summary (component summary), kinetic rate constant parent compound Z sink small t-test parameter suggests significantly different zero. suggests, agreement analysis FOCUS kinetics report, simplify model removing pathway sink. similar result can obtained formation fractions used model formulation:  , ilr transformed formation fraction fitted model takes large value, backtransformed formation fraction parent Z Z1 practically unity. , covariance matrix used calculation confidence intervals returned model overparameterised. simplified model obtained removing pathway sink. following, use parameterisation formation fractions order able compare results FOCUS guidance, makes easier use parameters obtained previous fit adding metabolite.  one transformation product Z0 pathway sink, formation fraction internally fixed unity.","code":"Z.2a <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\"),                 Z1 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.2a) summary(m.Z.2a, 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 ## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762 ## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000 ## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815 Z.2a.ff <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\"),                    Z1 = mkinsub(\"SFO\"),                    use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.2a.ff) summary(m.Z.2a.ff, data = FALSE)$bpar ##            Estimate se_notrans t value     Pr(>t)    Lower    Upper ## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642 ## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600 ## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762 ## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000 ## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815 Z.3 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                Z1 = mkinsub(\"SFO\"), use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.3) summary(m.Z.3, data = FALSE)$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 ## k_Z1   0.48212   0.041727  11.554 4.8268e-12  0.40355   0.5760 ## sigma  4.80411   0.620208   7.746 1.6110e-08  3.52925   6.0790"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"metabolites-z2-and-z3","dir":"Articles > Web_only","previous_headings":"","what":"Metabolites Z2 and Z3","title":"Example evaluation of FOCUS dataset Z","text":"suggested FOCUS report, pathway sink removed metabolite Z1 well next step. step appears questionable basis results, followed purpose comparison. Also, FOCUS report, assumed additional empirical evidence Z1 quickly exclusively hydrolyses Z2.  Finally, metabolite Z3 added model. use optimised differential equation parameter values previous fit order accelerate optimization.  fit corresponds final result chosen Appendix 7 FOCUS report. Confidence intervals returned mkin based internally transformed parameters, however.","code":"Z.5 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                Z2 = mkinsub(\"SFO\"), use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.5) Z.FOCUS <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                    Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                    Z2 = mkinsub(\"SFO\", \"Z3\"),                    Z3 = mkinsub(\"SFO\"),                    use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,                      parms.ini = m.Z.5$bparms.ode,                      quiet = TRUE) ## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : ## Observations with value of zero were removed from the data ## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge: ## false convergence (8) plot_sep(m.Z.FOCUS) summary(m.Z.FOCUS, data = FALSE)$bpar ##             Estimate se_notrans t value     Pr(>t)     Lower      Upper ## Z0_0       96.842440   1.994291 48.5598 4.0226e-42 92.830421 100.854459 ## k_Z0        2.215425   0.118457 18.7023 1.0404e-23  1.989490   2.467019 ## k_Z1        0.478307   0.028257 16.9272 6.2332e-22  0.424709   0.538669 ## k_Z2        0.451642   0.042139 10.7178 1.6304e-14  0.374348   0.544894 ## k_Z3        0.058692   0.015245  3.8499 1.7803e-04  0.034804   0.098975 ## f_Z2_to_Z3  0.471483   0.058348  8.0806 9.6585e-11  0.357720   0.588287 ## sigma       3.984431   0.383402 10.3923 4.5576e-14  3.213126   4.755737 endpoints(m.Z.FOCUS) ## $ff ##   Z2_Z3 Z2_sink  ## 0.47148 0.52852  ##  ## $distimes ##        DT50    DT90 ## Z0  0.31287  1.0393 ## Z1  1.44917  4.8140 ## Z2  1.53473  5.0983 ## Z3 11.80991 39.2317"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"using-the-sforb-model","dir":"Articles > Web_only","previous_headings":"","what":"Using the SFORB model","title":"Example evaluation of FOCUS dataset Z","text":"FOCUS report states, certain tailing time course metabolite Z3. Also, time course parent compound fitted well using SFO model, residues certain low level remain. Therefore, additional model offered , using single first-order reversible binding (SFORB) model metabolite Z3. expected, χ2\\chi^2 error level lower metabolite Z3 using model graphical fit Z3 improved. However, covariance matrix returned.  Therefore, stepwise model building performed starting stage parent two metabolites, starting assumption model fit parent compound can improved using SFORB model.  results much better representation behaviour parent compound Z0. Finally, Z3 added well. models appear overparameterised (covariance matrix returned) sink Z1 left models.  error level fit, especially metabolite Z3, can improved SFORB model chosen metabolite, model capable representing tailing metabolite decline phase.  summary view backtransformed parameters shows get confidence intervals due overparameterisation. optimized excessively small, seems reasonable fix zero.  expected, residual plots Z0 Z3 random case SFO model shown . conclusion, model proposed best-fit model dataset Appendix 7 FOCUS report. graphical representation confidence intervals can finally obtained.  endpoints obtained model clear degradation rate Z3 towards end experiment low DT50_Z3_b2 (second Eigenvalue system two differential equations representing SFORB system Z3, corresponding slower rate constant DFOP model) reported infinity. However, appears feature data.","code":"Z.mkin.1 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFORB\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations ## with value of zero were removed from the data plot_sep(m.Z.mkin.1) summary(m.Z.mkin.1, data = FALSE)$cov.unscaled ## NULL Z.mkin.3 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations ## with value of zero were removed from the data plot_sep(m.Z.mkin.3) Z.mkin.4 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,                       parms.ini = m.Z.mkin.3$bparms.ode,                       quiet = TRUE) ## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = ## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the ## data plot_sep(m.Z.mkin.4) Z.mkin.5 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFORB\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,                       parms.ini = m.Z.mkin.4$bparms.ode[1:4],                       quiet = TRUE) ## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = ## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from ## the data plot_sep(m.Z.mkin.5) m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,                        parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],                                      k_Z3_bound_free = 0),                        fixed_parms = \"k_Z3_bound_free\",                        quiet = TRUE) ## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = ## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed ## from the data plot_sep(m.Z.mkin.5a) mkinparplot(m.Z.mkin.5a) endpoints(m.Z.mkin.5a) ## $ff ## Z0_free   Z2_Z3 Z2_sink Z3_free  ## 1.00000 0.53656 0.46344 1.00000  ##  ## $SFORB ##     Z0_b1     Z0_b2      Z0_g     Z3_b1     Z3_b2      Z3_g  ## 2.4471342 0.0075124 0.9519866 0.0800071 0.0000000 0.9347816  ##  ## $distimes ##      DT50   DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 ## Z0 0.3043 1.1848  0.35666    0.28325     92.267         NA         NA ## Z1 1.5148 5.0320       NA         NA         NA         NA         NA ## Z2 1.6414 5.4526       NA         NA         NA         NA         NA ## Z3     NA     NA       NA         NA         NA     8.6636        Inf"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"document, example evaluations provided Attachment 1 SOP US EPA using NAFTA guidance (US EPA 2015) repeated using mkin. original evaluations reported attachment performed using PestDF version 0.8.4. Note PestDF 0.8.13 version distributed US EPA website today (2019-02-26). datasets now distributed mkin package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"examples-where-dfop-did-not-converge-with-pestdf-0-8-4","dir":"Articles > Web_only","previous_headings":"","what":"Examples where DFOP did not converge with PestDF 0.8.4","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"attachment 1, reported DFOP model converge datasets PestDF 0.8.4 used. four datasets, DFOP model can fitted mkin (see ). negative half-life given PestDF 0.8.4 fits appears result bug. results two models (SFO IORE) .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-5-upper-panel","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 5, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p5a) ## Sums of squares: ##       SFO      IORE      DFOP  ## 465.21753  56.27506  32.06401  ##  ## Critical sum of squares for checking the SFO model: ## [1] 64.4304 ##  ## Parameters: ## $SFO ##          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 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 ## g        6.47e-01 3.67e-06  0.6248   0.6677 ## sigma    1.27e+00 8.91e-06  0.8395   1.6929 ##  ##  ## DTx values: ##      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 ##  ## Representative half-life: ## [1] 321.51"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-5-lower-panel","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 5, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p5b) ## Sums of squares: ##      SFO     IORE     DFOP  ## 94.81123 10.10936  7.55871  ##  ## Critical sum of squares for checking the SFO model: ## [1] 11.77879 ##  ## Parameters: ## $SFO ##          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 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       9.07e-12 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 ##  ##  ## DTx values: ##      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 1.25e+11 7.64e+10 ##  ## Representative half-life: ## [1] 215.87"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-6","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 6","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p6) ## Sums of squares: ##       SFO      IORE      DFOP  ## 188.45361  51.00699  42.46931  ##  ## Critical sum of squares for checking the SFO model: ## [1] 58.39888 ##  ## Parameters: ## $SFO ##          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  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 ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1       2.55e-02 7.33e-06  0.0233  0.0278 ## k2       3.84e-11 5.00e-01  0.0000     Inf ## g        8.61e-01 7.55e-06  0.8314  0.8867 ## sigma    1.46e+00 6.93e-06  0.9661  1.9483 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 ## DFOP 34.1 8.50e+09 1.80e+10 ##  ## Representative half-life: ## [1] 53.17"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-7","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 7","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p7) ## Sums of squares: ##      SFO     IORE     DFOP  ## 3661.661 3195.030 3174.145  ##  ## Critical sum of squares for checking the SFO model: ## [1] 3334.194 ##  ## Parameters: ## $SFO ##          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 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       3.62e-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 ##  ##  ## DTx values: ##      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 3.79e+09 1.92e+09 ##  ## Representative half-life: ## [1] 454.55"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-8","dir":"Articles > Web_only","previous_headings":"Examples where the representative half-life deviates from the observed DT50","what":"Example on page 8","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"dataset, IORE fit converge default starting values used mkin IORE model used. Therefore, lower value rate constant used .","code":"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  ## 1996.9408  444.9237  547.5616  ##  ## Critical sum of squares for checking the SFO model: ## [1] 477.4924 ##  ## Parameters: ## $SFO ##          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 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 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: ##      DT50 DT90 DT50_rep ## SFO  86.3  287     86.3 ## IORE 53.4  668    201.0 ## DFOP 55.6  517    253.0 ##  ## Representative half-life: ## [1] 201.03"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-9-upper-panel","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 9, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"example, residuals SFO indicate lack fit model, even abiotic experiment, data suggest simple exponential decline.","code":"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) print(p9a) ## Sums of squares: ##       SFO      IORE      DFOP  ## 839.35238  88.57064   9.93363  ##  ## Critical sum of squares for checking the SFO model: ## [1] 105.5678 ##  ## Parameters: ## $SFO ##          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 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       9.02e-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 ##  ##  ## DTx values: ##      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 1.38e+12 7.68e+11 ##  ## Representative half-life: ## [1] 101.43"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-9-lower-panel","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 9, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":", mkin gives longer slow DT50 DFOP model (17.8 days) PestDF (13.5 days). Presumably, related fact PestDF gives negative value proportion fast degradation 0 1, inclusive. parameter called f PestDF g mkin. mkin, restricted interval 0 1.","code":"p9b <- nafta(NAFTA_SOP_Attachment[[\"p9b\"]]) ## 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  ## 35.64867 23.22334 35.64867  ##  ## Critical sum of squares for checking the SFO model: ## [1] 28.54188 ##  ## Parameters: ## $SFO ##          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    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 1.08e-04  0.0266  0.0569 ## k2         0.0389 2.24e-04  0.0255  0.0592 ## g          0.5256 5.00e-01  0.0000  1.0000 ## sigma      1.5957 2.50e-04  0.9135  2.2779 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  17.8 59.2     17.8 ## IORE 18.4 49.2     14.8 ## DFOP 17.8 59.2     17.8 ##  ## Representative half-life: ## [1] 14.8"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-10","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 10","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":", value N given IORE model, data suggests faster decline towards end experiment, appears physically rather unlikely case photolysis study. seems PestDF constrain N values zero, thus slight difference IORE model parameters PestDF mkin.","code":"p10 <- nafta(NAFTA_SOP_Attachment[[\"p10\"]]) ## Warning in sqrt(diag(covar_notrans)): NaNs produced ## 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  ## 899.4089 336.4348 899.4089  ##  ## Critical sum of squares for checking the SFO model: ## [1] 413.4841 ##  ## Parameters: ## $SFO ##          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     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 111.810 ## k1         0.0495 3.04e-03  0.0188   0.131 ## k2         0.0495 4.92e-04  0.0197   0.124 ## g          0.4487      NaN  0.0000   1.000 ## sigma      8.0152 2.50e-04  4.5886  11.442 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  14.0 46.5    14.00 ## IORE 16.4 29.4     8.86 ## DFOP 14.0 46.5    14.00 ##  ## Representative half-life: ## [1] 8.86"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-11","dir":"Articles > Web_only","previous_headings":"The DT50 was not observed during the study","what":"Example on page 11","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"case, DFOP fit reported PestDF resulted negative value slower rate constant, possible mkin. results agreement.","code":"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) print(p11) ## Sums of squares: ##      SFO     IORE     DFOP  ## 579.6805 204.7932 144.7783  ##  ## Critical sum of squares for checking the SFO model: ## [1] 251.6944 ##  ## Parameters: ## $SFO ##          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 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       9.94e-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 ##  ##  ## DTx values: ##          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 3.07e+11 1.93e+12 6.98e+11 ##  ## Representative half-life: ## [1] 41148169"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant","dir":"Articles > Web_only","previous_headings":"","what":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"following three examples, results obtained mkin reported PestDF. case page 10, N values 1 deemed unrealistic appear result overparameterisation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-12-upper-panel","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 12, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p12a <- nafta(NAFTA_SOP_Attachment[[\"p12a\"]]) ## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix ## Warning in sqrt(diag(covar_notrans)): NaNs produced ## 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  ## 695.4440 220.0685 695.4440  ##  ## Critical sum of squares for checking the SFO model: ## [1] 270.4679 ##  ## Parameters: ## $SFO ##          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    2.436     NA    NA    NA ## N_parent          0.263     NA    NA    NA ## sigma             3.965     NA    NA    NA ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0  100.521 2.74e-10 92.2366 108.805 ## k1          0.124 2.53e-05  0.0908   0.170 ## k2          0.124 2.52e-02  0.0456   0.339 ## g           0.793      NaN  0.0000   1.000 ## sigma       7.048 2.50e-04  4.0349  10.061 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  5.58 18.5     5.58 ## IORE 6.49 13.2     3.99 ## DFOP 5.58 18.5     5.58 ##  ## Representative half-life: ## [1] 3.99"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-12-lower-panel","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 12, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p12b <- nafta(NAFTA_SOP_Attachment[[\"p12b\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in qt(alpha/2, rdf): NaNs produced ## Warning in qt(1 - alpha/2, rdf): NaNs produced ## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## 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  ## 58.90242 19.06353 58.90242  ##  ## Critical sum of squares for checking the SFO model: ## [1] 51.51756 ##  ## Parameters: ## $SFO ##          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    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          0.6473    NaN    NA    NA ## sigma      3.4323    NaN   NaN   NaN ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  11.8 39.1    11.80 ## IORE 12.9 31.4     9.46 ## DFOP 11.8 39.1    11.80 ##  ## Representative half-life: ## [1] 9.46"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-13","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 13","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p13 <- nafta(NAFTA_SOP_Attachment[[\"p13\"]]) ## 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  ## 174.5971 142.3951 174.5971  ##  ## Critical sum of squares for checking the SFO model: ## [1] 172.131 ##  ## Parameters: ## $SFO ##          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   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 ##  ## $DFOP ##          Estimate Pr(>t)    Lower    Upper ## parent_0 92.73500     NA 8.95e+01 95.92118 ## k1        0.00258     NA 4.18e-04  0.01592 ## k2        0.00258     NA 1.75e-03  0.00381 ## g         0.16452     NA 0.00e+00  1.00000 ## sigma     3.41172     NA 2.02e+00  4.79960 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO   269  892      269 ## IORE  261  560      169 ## DFOP  269  892      269 ##  ## Representative half-life: ## [1] 168.51"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf","dir":"Articles > Web_only","previous_headings":"","what":"DT50 not observed in the study and DFOP problems in PestDF","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"slower rate constant reported PestDF negative, physically realistic, possible mkin. fits give results mkin PestDF.","code":"p14 <- nafta(NAFTA_SOP_Attachment[[\"p14\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## 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  ## 48.43249 28.67746 27.26248  ##  ## Critical sum of squares for checking the SFO model: ## [1] 32.83337 ##  ## Parameters: ## $SFO ##          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 9.44e-08     NA   NaN   NaN ## N_parent       3.31e+00     NA   NaN   NaN ## sigma          1.20e+00     NA 0.796   1.6 ##  ## $DFOP ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1       9.53e-03 1.20e-01  0.00638   0.0143 ## k2       5.21e-12 5.00e-01  0.00000      Inf ## g        3.98e-01 2.19e-01  0.30481   0.4998 ## sigma    1.17e+00 7.68e-06  0.77406   1.5610 ##  ##  ## DTx values: ##          DT50     DT90 DT50_rep ## SFO  2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 ## DFOP 3.55e+10 3.44e+11 1.33e+11 ##  ## Representative half-life: ## [1] 6697.44"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero","dir":"Articles > Web_only","previous_headings":"","what":"N is less than 1 and DFOP fraction parameter is below zero","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"mkin, IORE fit affected (deemed unrealistic), fraction parameter DFOP model restricted interval 0 1 mkin. SFO fits give results mkin PestDF.","code":"p15a <- nafta(NAFTA_SOP_Attachment[[\"p15a\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## 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  ## 245.5248 135.0132 245.5248  ##  ## Critical sum of squares for checking the SFO model: ## [1] 165.9335 ##  ## Parameters: ## $SFO ##          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    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 6.28e-02  0.00260   0.0349 ## k2        0.00952 1.27e-04  0.00652   0.0139 ## g         0.21241 5.00e-01       NA       NA ## sigma     4.18778 2.50e-04  2.39747   5.9781 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  72.8  242     72.8 ## IORE 76.3  137     41.3 ## DFOP 72.8  242     72.8 ##  ## Representative half-life: ## [1] 41.33 p15b <- nafta(NAFTA_SOP_Attachment[[\"p15b\"]]) ## 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(p15b) print(p15b) ## Sums of squares: ##       SFO      IORE      DFOP  ## 106.91629  68.55574 106.91629  ##  ## Critical sum of squares for checking the SFO model: ## [1] 84.25618 ##  ## Parameters: ## $SFO ##          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 97.51348 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    NA    NA ## k1       4.86e-03     NA    NA    NA ## k2       4.86e-03     NA    NA    NA ## g        1.88e-01     NA    NA    NA ## sigma    2.76e+00     NA    NA    NA ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO   143  474    143.0 ## IORE  131  236     71.2 ## DFOP  143  474    143.0 ##  ## Representative half-life: ## [1] 71.18"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"the-dfop-fraction-parameter-is-greater-than-1","dir":"Articles > Web_only","previous_headings":"","what":"The DFOP fraction parameter is greater than 1","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"PestDF, DFOP fit seems stuck local minimum, mkin finds solution much lower χ2\\chi^2 error level. half-life slower rate constant DFOP model larger IORE derived half-life, NAFTA recommendation obtained mkin use DFOP representative half-life 8.9 days.","code":"p16 <- nafta(NAFTA_SOP_Attachment[[\"p16\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## 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) print(p16) ## Sums of squares: ##      SFO     IORE     DFOP  ## 3831.804 2062.008 1550.980  ##  ## Critical sum of squares for checking the SFO model: ## [1] 2247.348 ##  ## Parameters: ## $SFO ##          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 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        18.8461 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 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  4.35 14.4     4.35 ## IORE 1.48 32.1     9.67 ## DFOP 0.67 21.4     8.93 ##  ## Representative half-life: ## [1] 8.93"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"results obtained mkin deviate results obtained PestDF either cases one interpretive rules apply, .e. IORE parameter N less one DFOP k values obtained PestDF equal SFO k values, cases DFOP model converge, often lead negative rate constants returned PestDF. Therefore, mkin appears suitable kinetic evaluations according NAFTA guidance.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"test-cases","dir":"Articles > Web_only","previous_headings":"","what":"Test cases","title":"Benchmark timings for mkin","text":"Parent : One metabolite: Two metabolites, synthetic data:","code":"FOCUS_C <- FOCUS_2006_C FOCUS_D <- subset(FOCUS_2006_D, value != 0) parent_datasets <- list(FOCUS_C, FOCUS_D)   t1 <- system.time(mmkin_bench(c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\"), parent_datasets))[[\"elapsed\"]] t2 <- system.time(mmkin_bench(c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\"), parent_datasets,     error_model = \"tc\"))[[\"elapsed\"]] SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) FOMC_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"),   m1 = mkinsub(\"SFO\")) DFOP_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"), # erroneously used FOMC twice, not fixed for consistency   m1 = mkinsub(\"SFO\")) t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[[\"elapsed\"]] t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),     error_model = \"tc\"))[[\"elapsed\"]] t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),     error_model = \"obs\"))[[\"elapsed\"]] m_synth_SFO_lin <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                            M1 = mkinsub(\"SFO\", \"M2\"),                            M2 = mkinsub(\"SFO\"),                            use_of_ff = \"max\", quiet = TRUE)  m_synth_DFOP_par <- mkinmod(parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),                            M1 = mkinsub(\"SFO\"),                            M2 = mkinsub(\"SFO\"),                            use_of_ff = \"max\", quiet = TRUE)  SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data  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\"]]  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\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"results","dir":"Articles > Web_only","previous_headings":"","what":"Results","title":"Benchmark timings for mkin","text":"Benchmarks available error models shown. intended improving mkin, comparing CPUs operating systems. trademarks belong respective owners.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"parent-only","dir":"Articles > Web_only","previous_headings":"Results","what":"Parent only","title":"Benchmark timings for mkin","text":"Constant variance (t1) two-component error model (t2) four models fitted two datasets, .e. eight fits test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"one-metabolite","dir":"Articles > Web_only","previous_headings":"Results","what":"One metabolite","title":"Benchmark timings for mkin","text":"Constant variance (t3), two-component error model (t4), variance variable (t5) three models fitted one dataset, .e. three fits test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"two-metabolites","dir":"Articles > Web_only","previous_headings":"Results","what":"Two metabolites","title":"Benchmark timings for mkin","text":"Constant variance (t6 t7), two-component error model (t8 t9), variance variable (t10 t11) one model fitted one dataset, .e. one fit test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"how-to-benefit-from-compiled-models","dir":"Articles > Web_only","previous_headings":"","what":"How to benefit from compiled models","title":"Performance benefit by using compiled model definitions in mkin","text":"using mkin version equal greater 0.9-36 C compiler available, see message model compiled autogenerated C code defining model using mkinmod. Starting version 0.9.49.9, mkinmod() function checks presence compiler using previous versions, used Sys.(\"gcc\") check. Linux, need essential build tools like make gcc clang installed. Debian based linux distributions, pulled installing build-essential package. MacOS, use personally, reports compiler available default. Windows, need install Rtools path bin directory PATH variable. need modify PATH variable installing Rtools. Instead, recommend put line .Rprofile startup file. just text file R code executed R session starts. named .Rprofile located home directory, generally Documents folder. can check location home directory used R issuing","code":"pkgbuild::has_compiler() Sys.setenv(PATH = paste(\"C:/Rtools/bin\", Sys.getenv(\"PATH\"), sep=\";\")) Sys.getenv(\"HOME\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"comparison-with-other-solution-methods","dir":"Articles > Web_only","previous_headings":"","what":"Comparison with other solution methods","title":"Performance benefit by using compiled model definitions in mkin","text":"First, build simple degradation model parent compound one metabolite, remove zero values dataset. can compare performance Eigenvalue based solution compiled version R implementation differential equations using benchmark package. output code, warnings zero removed FOCUS D dataset suppressed. Since mkin version 0.9.49.11, analytical solution also implemented, included tests . see using compiled model factor 10 faster using deSolve without compiled code.","code":"library(\"mkin\", quietly = TRUE) SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) if (require(rbenchmark)) {   b.1 <- benchmark(     \"deSolve, not compiled\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"deSolve\",        use_compiled = FALSE, quiet = TRUE),     \"Eigenvalue based\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"eigen\", quiet = TRUE),     \"deSolve, compiled\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"deSolve\", quiet = TRUE),     \"analytical\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"analytical\",        use_compiled = FALSE, quiet = TRUE),     replications = 1, order = \"relative\",     columns = c(\"test\", \"replications\", \"relative\", \"elapsed\"))   print(b.1) } else {   print(\"R package rbenchmark is not available\") } ##                    test replications relative elapsed ## 4            analytical            1    1.000   0.111 ## 3     deSolve, compiled            1    1.270   0.141 ## 2      Eigenvalue based            1    1.658   0.184 ## 1 deSolve, not compiled            1   21.973   2.439"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"model-without-analytical-solution","dir":"Articles > Web_only","previous_headings":"","what":"Model without analytical solution","title":"Performance benefit by using compiled model definitions in mkin","text":"evaluation also taken example section mkinfit. analytical solution available system, now Eigenvalue based solution possible, deSolve using without compiled code available. get performance benefit factor 24 using version differential equation model compiled C code! vignette built mkin 1.2.10 ","code":"if (require(rbenchmark)) {   FOMC_SFO <- mkinmod(     parent = mkinsub(\"FOMC\", \"m1\"),     m1 = mkinsub( \"SFO\"))    b.2 <- benchmark(     \"deSolve, not compiled\" = mkinfit(FOMC_SFO, FOCUS_D,                                       use_compiled = FALSE, quiet = TRUE),     \"deSolve, compiled\" = mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE),     replications = 1, order = \"relative\",     columns = c(\"test\", \"replications\", \"relative\", \"elapsed\"))   print(b.2)   factor_FOMC_SFO <- round(b.2[\"1\", \"relative\"]) } else {   factor_FOMC_SFO <- NA   print(\"R package benchmark is not available\") } ## Temporary DLL for differentials generated and loaded ##                    test replications relative elapsed ## 2     deSolve, compiled            1    1.000   0.182 ## 1 deSolve, not compiled            1   23.808   4.333 ## R version 4.5.0 (2025-04-11) ## Platform: x86_64-pc-linux-gnu ## Running under: Debian GNU/Linux 12 (bookworm) ## CPU model: AMD Ryzen 9 7950X 16-Core Processor"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Example evaluations of the dimethenamid data from 2018","text":"first analysis data analysed presented recent journal article nonlinear mixed-effects models degradation kinetics (Ranke et al. 2021). analysis based nlme package development version saemix package unpublished time. Meanwhile, version 3.0 saemix package available CRAN repository. Also, turned error handling Borstel data mkin package time, leading duplication data points soil. dataset mkin package corrected, interface saemix mkin package updated use released version. vignette intended present date analysis data, using corrected dataset released versions mkin saemix.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"data","dir":"Articles > Web_only","previous_headings":"","what":"Data","title":"Example evaluations of the dimethenamid data from 2018","text":"Residue data forming basis endpoints derived conclusion peer review pesticide risk assessment dimethenamid-P published European Food Safety Authority (EFSA) 2018 (EFSA 2018) transcribed risk assessment report (Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018) can downloaded Open EFSA repository https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716. data available mkin package. following code (hidden default, please use button right show ) treats data available racemic mixture dimethenamid (DMTA) enantiomer dimethenamid-P (DMTAP) way, difference degradation behaviour identified EU risk assessment. observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters. Also, datasets observed soil merged, resulting dimethenamid (DMTA) data six soils.","code":"library(mkin, quietly = TRUE) dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"parent-degradation","dir":"Articles > Web_only","previous_headings":"","what":"Parent degradation","title":"Example evaluations of the dimethenamid data from 2018","text":"evaluate observed degradation parent compound using simple exponential decline (SFO) biexponential decline (DFOP), using constant variance (const) two-component variance (tc) error models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Separate evaluations","title":"Example evaluations of the dimethenamid data from 2018","text":"first step, get visual impression fit different models, separate evaluations soil using mmkin function mkin package: plot individual SFO fits shown suggests least datasets degradation slows towards later time points, scatter residuals error smaller smaller values (panel right):  Using biexponential decline (DFOP) results slightly random scatter residuals:  population curve (bold line) plot results taking mean individual transformed parameters, .e. log k1 log k2, well logit g parameter DFOP model). , procedure result parameters represent degradation well, datasets fitted value k2 extremely close zero, leading log k2 value dominates average. alleviated rate constants pass t-test significant difference zero (untransformed scale) considered averaging:  visually much satisfactory, average procedure introduce bias, results individual fits enter population curve weight. nonlinear mixed-effects models can help treating datasets equally fitting parameter distribution model together degradation model error model (see ). remaining trend residuals higher higher predicted residues reduced using two-component error model:  However, note case using error model, fits Flaach BBA 2.3 datasets appear ill-defined, indicated fact converge:","code":"f_parent_mkin_const <- mmkin(c(\"SFO\", \"DFOP\"), dmta_ds,   error_model = \"const\", quiet = TRUE) f_parent_mkin_tc <- mmkin(c(\"SFO\", \"DFOP\"), dmta_ds,   error_model = \"tc\", quiet = TRUE) plot(mixed(f_parent_mkin_const[\"SFO\", ])) plot(mixed(f_parent_mkin_const[\"DFOP\", ])) plot(mixed(f_parent_mkin_const[\"DFOP\", ]), test_log_parms = TRUE) plot(mixed(f_parent_mkin_tc[\"DFOP\", ]), test_log_parms = TRUE) print(f_parent_mkin_tc[\"DFOP\", ]) <mmkin> object Status of individual fits:        dataset model  Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot   DFOP OK    OK      OK     OK      C       OK      C: Optimisation did not converge: iteration limit reached without convergence (10) OK: No warnings"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"nonlinear-mixed-effects-models","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Nonlinear mixed-effects models","title":"Example evaluations of the dimethenamid data from 2018","text":"Instead taking model selection decision individual fits, fit nonlinear mixed-effects models (using different fitting algorithms implemented different packages) model selection using available data time. order make sure decisions unduly influenced type algorithm used, implementation details use wrong control parameters, compare model selection results obtained different R packages, different algorithms checking control parameters.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"nlme","dir":"Articles > Web_only","previous_headings":"Parent degradation > Nonlinear mixed-effects models","what":"nlme","title":"Example evaluations of the dimethenamid data from 2018","text":"nlme package first R extension providing facilities fit nonlinear mixed-effects models. like model selection four combinations degradation models error models based AIC. However, fitting DFOP model constant variance using default control parameters results error, signalling maximum number 50 iterations reached, potentially indicating overparameterisation. Nevertheless, algorithm converges two-component error model used combination DFOP model. can explained fact smaller residues observed later sampling times get weight using two-component error model counteract tendency algorithm try parameter combinations unsuitable fitting data. Note certain degree overparameterisation also indicated warning obtained fitting DFOP two-component error model (‘false convergence’ ‘LME step’ iteration 3). However, warning occur later iterations, specifically last 5 iterations, can ignore warning. model comparison function nlme package can directly applied fits showing much lower AIC DFOP model fitted two-component error model. Also, likelihood ratio test indicates difference significant p-value 0.0001. addition fits, attempts also made include correlations random effects using log Cholesky parameterisation matrix specifying . code used attempts can made visible . SFO variants converge fast, additional parameters introduced lead convergence warnings DFOP model. model comparison clearly show adding correlations random effects improve fits. selected model (DFOP two-component error) fitted data assuming correlations random effects shown .","code":"library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const[\"SFO\", ]) # f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const[\"DFOP\", ]) f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc[\"SFO\", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc[\"DFOP\", ]) anova(   f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc ) Model df    AIC    BIC  logLik   Test L.Ratio p-value f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                        f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998 f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.95 2 vs 3  134.69  <.0001 f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const[\"SFO\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc[\"SFO\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const[\"DFOP\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol) plot(f_parent_nlme_dfop_tc)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"saemix","dir":"Articles > Web_only","previous_headings":"Parent degradation > Nonlinear mixed-effects models","what":"saemix","title":"Example evaluations of the dimethenamid data from 2018","text":"saemix package provided first Open Source implementation Stochastic Approximation Expectation Maximisation (SAEM) algorithm. SAEM fits degradation models can conveniently performed using interface saemix package available current development versions mkin package. corresponding SAEM fits four combinations degradation error models fitted . convergence criterion implemented saemix package, convergence plots need manually checked every fit. define control settings work well parent data fits shown vignette. convergence plot SFO model using constant variance shown .  Obviously selected number iterations sufficient reach convergence. can also said SFO fit using two-component error model.  fitting DFOP model constant variance (see ), parameter convergence unambiguous.  parameters converge credible values, variance k2 (omega2.k2) converges small value. printout saem.mmkin model shows estimated standard deviation k2 across population soils (SD.k2) ill-defined, indicating overparameterisation model. DFOP model fitted two-component error model, also observe estimated variance k2 becomes small, ill-defined, illustrated excessive confidence interval SD.k2.  Doubling number iterations first phase algorithm leads slightly lower likelihood, therefore slightly higher AIC BIC values. even iterations, algorithm stops error message. related variance k2 approximating zero submitted bug saemix package, algorithm converge case. alternative way fit DFOP combination two-component error model use model formulation transformed parameters used per default mkin. using option, convergence slower, eventually algorithm stops well error message. four combinations (SFO/const, SFO/tc, DFOP/const DFOP/tc) version increased iterations can compared using model comparison function saemix package: order check influence likelihood calculation algorithms implemented saemix, likelihood Gaussian quadrature added best fit, AIC values obtained three methods compared. AIC values based importance sampling Gaussian quadrature similar. Using linearisation known less accurate, still gives similar value. order illustrate comparison three method depends degree convergence obtained fit, comparison shown fit using defaults number iterations number MCMC chains. using OpenBlas linear algebra, large difference values obtained Gaussian quadrature, larger number iterations makes lot difference. using LAPACK version coming Debian Bullseye, AIC based Gaussian quadrature almost one obtained methods, also using defaults fit.","code":"library(saemix) saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const[\"SFO\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_sfo_const$so, plot.type = \"convergence\") f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc[\"SFO\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_sfo_tc$so, plot.type = \"convergence\") f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const[\"DFOP\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_dfop_const$so, plot.type = \"convergence\") print(f_parent_saemix_dfop_const) Kinetic nonlinear mixed-effects model fit by SAEM Structural model: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Likelihood computed by importance sampling   AIC BIC logLik   706 704   -344  Fitted parameters:           estimate    lower   upper DMTA_0    97.99583 96.50079 99.4909 k1         0.06377  0.03432  0.0932 k2         0.00848  0.00444  0.0125 g          0.95701  0.91313  1.0009 a.1        1.82141  1.60516  2.0377 SD.DMTA_0  1.64787  0.45729  2.8384 SD.k1      0.57439  0.24731  0.9015 SD.k2      0.03296 -2.50524  2.5712 SD.g       1.10266  0.32354  1.8818 f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ], quiet = TRUE,   control = saemix_control_moreiter, transformations = \"saemix\") plot(f_parent_saemix_dfop_tc$so, plot.type = \"convergence\") print(f_parent_saemix_dfop_tc) Kinetic nonlinear mixed-effects model fit by SAEM Structural model: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Likelihood computed by importance sampling   AIC BIC logLik   666 664   -323  Fitted parameters:           estimate     lower    upper DMTA_0    98.24165  96.29190 100.1914 k1         0.06421   0.03352   0.0949 k2         0.00866   0.00617   0.0111 g          0.95340   0.91218   0.9946 a.1        1.06463   0.87979   1.2495 b.1        0.02964   0.02266   0.0366 SD.DMTA_0  2.03611   0.40361   3.6686 SD.k1      0.59534   0.25692   0.9338 SD.k2      0.00042 -73.00540  73.0062 SD.g       1.04234   0.37189   1.7128 AIC_parent_saemix <- saemix::compare.saemix(   f_parent_saemix_sfo_const$so,   f_parent_saemix_sfo_tc$so,   f_parent_saemix_dfop_const$so,   f_parent_saemix_dfop_tc$so,   f_parent_saemix_dfop_tc_moreiter$so) Likelihoods calculated by importance sampling rownames(AIC_parent_saemix) <- c(   \"SFO const\", \"SFO tc\", \"DFOP const\", \"DFOP tc\", \"DFOP tc more iterations\") print(AIC_parent_saemix) AIC    BIC SFO const               796.38 795.34 SFO tc                  798.38 797.13 DFOP const              705.75 703.88 DFOP tc                 665.67 663.59 DFOP tc more iterations 665.85 663.76 f_parent_saemix_dfop_tc$so <-   saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) AIC_parent_saemix_methods <- c(   is = AIC(f_parent_saemix_dfop_tc$so, method = \"is\"),   gq = AIC(f_parent_saemix_dfop_tc$so, method = \"gq\"),   lin = AIC(f_parent_saemix_dfop_tc$so, method = \"lin\") ) print(AIC_parent_saemix_methods) is     gq    lin  665.67 665.74 665.13 f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ]) f_parent_saemix_dfop_tc_defaults$so <-   saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so) AIC_parent_saemix_methods_defaults <- c(   is = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"is\"),   gq = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"gq\"),   lin = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"lin\") ) print(AIC_parent_saemix_methods_defaults) is     gq    lin  670.09 669.37 671.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"comparison","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Comparison","title":"Example evaluations of the dimethenamid data from 2018","text":"following table gives AIC values obtained backend packages using control parameters (800 iterations burn-, 300 iterations second phase, 15 chains).","code":"AIC_all <- data.frame(   check.names = FALSE,   \"Degradation model\" = c(\"SFO\", \"SFO\", \"DFOP\", \"DFOP\"),   \"Error model\" = c(\"const\", \"tc\", \"const\", \"tc\"),   nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),   saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = \"lin\"),   saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = \"is\") ) kable(AIC_all)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"conclusion","dir":"Articles > Web_only","previous_headings":"","what":"Conclusion","title":"Example evaluations of the dimethenamid data from 2018","text":"detailed analysis dimethenamid dataset confirmed DFOP model provides appropriate description decline parent compound data. hand, closer inspection results revealed variability k2 parameter across population soils ill-defined. coincides observation parameter robustly quantified soils. Regarding regulatory use data, claimed improved characterisation mean parameter values across population obtained using nonlinear mixed-effects models presented . However, attempts quantify variability slower rate constant biphasic decline dimethenamid indicate data sufficient characterise variability satisfactory precision.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"","what":"Session Info","title":"Example evaluations of the dimethenamid data from 2018","text":"","code":"sessionInfo() R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] stats     graphics  grDevices utils     datasets  methods   base       other attached packages: [1] saemix_3.3   npde_3.5     nlme_3.1-168 mkin_1.2.10  knitr_1.49    loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  parallel_4.5.0    gridExtra_2.3     jquerylib_0.1.4    [9] systemfonts_1.2.1 scales_1.3.0      textshaping_1.0.0 yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.9.0       [25] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51         [29] fs_1.6.5          sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     [33] magrittr_2.0.3    digest_0.6.37     grid_4.5.0        mclust_6.1.1      [37] lifecycle_1.0.4   vctrs_0.6.5       evaluate_1.0.3    glue_1.8.0        [41] codetools_0.2-20  ragg_1.3.3        zoo_1.8-13        colorspace_2.1-1  [45] rmarkdown_2.29    tools_4.5.0       pkgconfig_2.0.3   htmltools_0.5.8.1"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package, also considering influence covariates like soil pH different degradation parameters. case studies, SFORB parameterisation biexponential decline shown advantages DFOP parameterisation, SFORB included list tested models well. mkin package used version 1.2.11, contains functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"Introduction","what":"Test data","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following tables show covariate data 18 datasets read spreadsheet file. Covariate data Dataset Richmond Dataset Richmond 2 Dataset ERTC Dataset Toulouse Dataset Picket Piece Dataset 721 Dataset 722 Dataset 723 Dataset 724 Dataset 725 Dataset 727 Dataset 728 Dataset 729 Dataset 730 Dataset 731 Dataset 732 Dataset 741 Dataset 742","code":"data_file <- system.file(   \"testdata\", \"mesotrione_soil_efsa_2016.xlsx\", package = \"mkin\") meso_ds <- read_spreadsheet(data_file, parent_only = TRUE) pH <- attr(meso_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(meso_ds)) {   print(     kable(mkin_long_to_wide(meso_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE)) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"","what":"Separate evaluations","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"order obtain suitable starting parameters NLHM fits, separate fits five models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. tables , OK indicates convergence C indicates failure converge. separate fits constant variance converged, exception two FOMC fits, one SFORB fit one HS fit. two-component error model, set fits converge larger, convergence problems appearing number non-SFO fits.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\") f_sep_const <- mmkin(   deg_mods,   meso_ds,   error_model = \"const\",   cluster = cl,   quiet = TRUE) status(f_sep_const[, 1:5]) |> kable() status(f_sep_const[, 6:18]) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc[, 1:5]) |> kable() status(f_sep_tc[, 6:18]) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hierarchical-models-without-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models without covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following code fits hierarchical kinetic models ten combinations five different degradation models two different error models parallel. fits terminate without errors (status OK). model comparisons show fits constant variance consistently preferable corresponding fits two-component error data. confirmed fact parameter b.1 (relative standard deviation fits obtained saemix package), ill-defined fits. obtaining fits well-defined random effects, update set fits, excluding random effects degradation parameters ill-defined according illparms function. updated fits terminate without errors, ill-defined ill-defined errors remain fits constant variance.","code":"f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) status(f_saem_1) |> kable() anova(f_saem_1) |> kable(digits = 1) illparms(f_saem_1) |> kable() f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hierarchical-models-with-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models with covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following sections, hierarchical fits including model influence pH selected degradation parameters shown parent models. Constant variance selected error model based fits without covariate effects. Random effects ill-defined fits without pH influence excluded. potential influence soil pH included parameters well-defined random effect, experience shown parameters significant pH effect found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"sfo","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFO","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"parameter showing pH influence table beta_pH(log_k_meso). confidence interval include zero, indicating influence soil pH log degradation rate constant significantly greater zero. comparison SFO fit without covariate effect confirms considering soil pH improves model, comparison AIC BIC likelihood ratio test.  Endpoints model covariates default calculated median covariate values. quantile can adapted, specific covariate value can given shown .","code":"sfo_pH <- saem(f_sep_const[\"SFO\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_k_meso ~ pH), center_covariates = \"median\") summary(sfo_pH)$confint_trans |> kable(digits = 2) anova(f_saem_2[[\"SFO\", \"const\"]], sfo_pH, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"SFO\", \"const\"]]    4 797.56 801.12 -394.78                          sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(sfo_pH) endpoints(sfo_pH) $covariates       pH 50% 5.75  $distimes          DT50     DT90 meso 18.52069 61.52441 endpoints(sfo_pH, covariate_quantile = 0.9) $covariates       pH 90% 7.13  $distimes          DT50     DT90 meso 8.237019 27.36278 endpoints(sfo_pH, covariates = c(pH = 7.0)) $covariates      pH User  7  $distimes         DT50    DT90 meso 8.89035 29.5331"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"fomc","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"FOMC","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"case SFO, confidence interval slope parameter (beta_pH(log_alpha)) quantifying influence soil pH include zero, model comparison clearly indicates model covariate influence preferable. However, random effect alpha well-defined inclusion covariate effect (confidence interval SD.log_alpha includes zero). Therefore, model updated without random effect, ill-defined parameters remain. Model comparison indicates including pH dependence significantly improves fit, reduced model covariate influence results preferable FOMC fit.","code":"fomc_pH <- saem(f_sep_const[\"FOMC\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_alpha ~ pH), center_covariates = \"median\") summary(fomc_pH)$confint_trans |> kable(digits = 2) illparms(fomc_pH) [1] \"sd(log_alpha)\" fomc_pH_2 <- update(fomc_pH, no_random_effect = c(\"meso_0\", \"log_alpha\")) illparms(fomc_pH_2) anova(f_saem_2[[\"FOMC\", \"const\"]], fomc_pH, fomc_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"FOMC\", \"const\"]]    5 783.25 787.71 -386.63                          fomc_pH_2                      6 766.50 771.84 -377.25 18.753  1  1.488e-05 *** fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(fomc_pH_2)$confint_trans |> kable(digits = 2) plot(fomc_pH_2) endpoints(fomc_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back meso 16.86448 83.26704 25.06588 endpoints(fomc_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back meso 7.835768 31.46451  9.47176"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"dfop","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"DFOP","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"DFOP fits without covariate effects, random effects two degradation parameters (k2 g) identifiable. fit pH dependent degradation parameters obtained excluding random effects refined DFOP fit without covariate influence, including covariate models two identifiable parameters k2 g. corresponding parameters influence soil pH beta_pH(log_k2) influence soil pH k2, beta_pH(g_qlogis) influence g. Confidence intervals neither include zero, indicating significant difference zero. However, random effect g now ill-defined. fit updated without ill-defined random effect. Now, slope parameter pH effect g ill-defined. Therefore, another attempt made without corresponding covariate model. random effect g ill-defined, fit repeated without . ill-defined parameters remain, model comparison suggests previous model dfop_pH_2 two pH dependent parameters preferable, based information criteria well based likelihood ratio test. focussing parameter identifiability using test confidence interval includes zero, dfop_pH_4 still preferred model. However, kept mind parameter confidence intervals constructed using simple linearisation likelihood. confidence interval random effect g marginally includes zero, suggested acceptable, dfop_pH_2 can considered preferable model.","code":"summary(f_saem_2[[\"DFOP\", \"const\"]])$confint_trans |> kable(digits = 2) dfop_pH <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH),   center_covariates = \"median\" ) summary(dfop_pH)$confint_trans |> kable(digits = 2) illparms(dfop_pH) [1] \"sd(g_qlogis)\" dfop_pH_2 <- update(dfop_pH,   no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_2) [1] \"beta_pH(g_qlogis)\" dfop_pH_3 <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH),   center_covariates = \"median\") illparms(dfop_pH_3) [1] \"sd(g_qlogis)\" dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_4) anova(f_saem_2[[\"DFOP\", \"const\"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik f_saem_2[[\"DFOP\", \"const\"]]    7 782.94 789.18 -384.47 dfop_pH_4                      7 767.35 773.58 -376.68 dfop_pH_2                      8 765.06 772.18 -374.53 dfop_pH_3                      8 769.00 776.12 -376.50 dfop_pH                        9 769.10 777.11 -375.55 anova(dfop_pH_2, dfop_pH_4, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)   dfop_pH_4    7 767.35 773.58 -376.68                        dfop_pH_2    8 765.06 772.18 -374.53 4.2909  1    0.03832 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(dfop_pH_2) endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.46687 74.91602 22.55197 4.568623 24.64483 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.370528 28.37824 8.542701 4.568623 8.785409"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"sforb","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFORB","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"confidence interval beta_pH(log_k_meso_bound_free) includes zero, indicating influence soil pH k_meso_bound_free reliably quantified. Also, confidence interval random effect parameter (SD.log_k_meso_bound_free) includes zero. Using illparms function, ill-defined parameters can found conveniently. remove ill-defined parameters, second variant SFORB model pH influence fitted. ill-defined parameters remain. model comparison SFORB fits includes refined model without covariate effect, versions SFORB fit covariate effect. first model including pH influence preferable based information criteria likelihood ratio test. However, fully identifiable, second model selected.","code":"sforb_pH <- saem(f_sep_const[\"SFORB\", ], no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\"),   covariates = pH,   covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH),   center_covariates = \"median\") summary(sforb_pH)$confint_trans |> kable(digits = 2) illparms(sforb_pH) [1] \"sd(log_k_meso_bound_free)\"      \"beta_pH(log_k_meso_bound_free)\" sforb_pH_2 <- update(sforb_pH,   no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\", \"log_k_meso_bound_free\"),   covariate_models = list(log_k_meso_free ~ pH)) illparms(sforb_pH_2) anova(f_saem_2[[\"SFORB\", \"const\"]], sforb_pH, sforb_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                               npar    AIC    BIC     Lik   Chisq Df Pr(>Chisq)   f_saem_2[[\"SFORB\", \"const\"]]    7 783.40 789.63 -384.70                         sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0              sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(sforb_pH_2)$confint_trans |> kable(digits = 2) plot(sforb_pH_2) endpoints(sforb_pH_2) $covariates       pH 50% 5.75  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.09735824 0.02631699 0.31602120   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.86549 73.15824 22.02282     7.119554     26.33839 endpoints(sforb_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.13315233 0.03795988 0.61186191   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 7.932495 36.93311 11.11797     5.205671        18.26"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hs","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"HS","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"According output illparms function, random effect break time tb reliably quantified, neither can influence soil pH tb. fit repeated without corresponding covariate model, ill-defined parameters remain. Model comparison confirms model preferable fit without covariate influence, also first version covariate influence.","code":"hs_pH <- saem(f_sep_const[\"HS\", ], no_random_effect = c(\"meso_0\"),   covariates = pH,   covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH),   center_covariates = \"median\") summary(hs_pH)$confint_trans |> kable(digits = 2) illparms(hs_pH) [1] \"sd(log_tb)\"      \"beta_pH(log_tb)\" hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) illparms(hs_pH_2) anova(f_saem_2[[\"HS\", \"const\"]], hs_pH, hs_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"HS\", \"const\"]]    8 780.08 787.20 -382.04                          hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 *** hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(hs_pH_2)$confint_trans |> kable(digits = 2) plot(hs_pH_2) endpoints(hs_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 14.68725 82.45287 24.82079 14.68725 29.29299 endpoints(hs_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.298536 38.85371 11.69613 8.298536 15.71561"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"comparison-across-parent-models","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"Comparison across parent models","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"model reduction models pH influence, compared . DFOP model pH influence k2 g random effect k2 finally selected best fit. endpoints resulting model listed . Please refer Appendix detailed listing.","code":"anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2) Data: 116 observations of 1 variable(s) grouped in 18 datasets             npar    AIC    BIC     Lik sfo_pH        5 783.09 787.54 -386.54 fomc_pH_2     6 766.50 771.84 -377.25 dfop_pH_4     7 767.35 773.58 -376.68 sforb_pH_2    7 770.94 777.17 -378.47 dfop_pH_2     8 765.06 772.18 -374.53 hs_pH_2      10 766.47 775.37 -373.23 endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.46687 74.91602 22.55197 4.568623 24.64483 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.370528 28.37824 8.542701 4.568623 8.785409"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"evaluations demonstrate covariate effects can included types parent degradation models. models can refined make fully identifiable.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"fits-without-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits without covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchical SFO fit constant variance  Hierarchical FOMC fit constant variance  Hierarchical DFOP fit constant variance  Hierarchical SFORB fit constant variance  Hierarchical HS fit constant variance","code":"saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:01 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.602 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling   AIC   BIC logLik   800 804.5   -395  Optimised parameters:                  est.    lower   upper meso_0        92.0705  89.9917 94.1493 log_k_meso    -3.1641  -3.4286 -2.8996 a.1            5.4628   4.6421  6.2835 SD.meso_0      0.0611 -98.3545 98.4767 SD.log_k_meso  0.5616   0.3734  0.7499  Correlation:             meso_0 log_k_meso 0.1132  Random effects:                 est.    lower   upper SD.meso_0     0.0611 -98.3545 98.4767 SD.log_k_meso 0.5616   0.3734  0.7499  Variance model:      est. lower upper a.1 5.463 4.642 6.284  Backtransformed parameters:            est.    lower    upper meso_0 92.07053 89.99172 94.14933 k_meso  0.04225  0.03243  0.05505  Estimated disappearance times:       DT50 DT90 meso 16.41 54.5 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:02 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.983 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 793.6 -386.7  Optimised parameters:                 est.     lower   upper meso_0       93.5648  91.42864 95.7009 log_alpha     0.7645   0.28068  1.2484 log_beta      3.6597   3.05999  4.2594 a.1           5.0708   4.29823  5.8435 SD.meso_0     0.1691 -34.01517 34.3535 SD.log_alpha  0.3764   0.05834  0.6945 SD.log_beta   0.3903  -0.06074  0.8414  Correlation:            meso_0  log_lph log_alpha -0.2839         log_beta  -0.3443  0.8855  Random effects:                est.     lower   upper SD.meso_0    0.1691 -34.01517 34.3535 SD.log_alpha 0.3764   0.05834  0.6945 SD.log_beta  0.3903  -0.06074  0.8414  Variance model:      est. lower upper a.1 5.071 4.298 5.843  Backtransformed parameters:          est.  lower  upper meso_0 93.565 91.429 95.701 alpha   2.148  1.324  3.485 beta   38.850 21.327 70.770  Estimated disappearance times:      DT50  DT90 DT50back meso 14.8 74.64    22.47 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:02 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.202 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.6 795.6 -384.8  Optimised parameters:                est.     lower   upper meso_0      93.6684  91.63599 95.7008 log_k1      -1.7354  -2.61433 -0.8565 log_k2      -3.4015  -3.73323 -3.0697 g_qlogis    -1.6341  -2.66133 -0.6069 a.1          4.7803   4.01269  5.5479 SD.meso_0    0.1661 -30.97086 31.3031 SD.log_k1    0.1127  -2.59680  2.8223 SD.log_k2    0.6394   0.41499  0.8638 SD.g_qlogis  0.8166   0.09785  1.5353  Correlation:           meso_0  log_k1  log_k2  log_k1    0.1757                 log_k2    0.0199  0.2990         g_qlogis  0.0813 -0.7431 -0.3826  Random effects:               est.     lower   upper SD.meso_0   0.1661 -30.97086 31.3031 SD.log_k1   0.1127  -2.59680  2.8223 SD.log_k2   0.6394   0.41499  0.8638 SD.g_qlogis 0.8166   0.09785  1.5353  Variance model:     est. lower upper a.1 4.78 4.013 5.548  Backtransformed parameters:            est.    lower    upper meso_0 93.66841 91.63599 95.70082 k1      0.17633  0.07322  0.42466 k2      0.03332  0.02392  0.04643 g       0.16327  0.06529  0.35277  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 16.04 63.75    19.19   3.931    20.8 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:02 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.557 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 795.4 -384.7  Optimised parameters:                             est.    lower  upper meso_free_0              93.6285  91.6262 95.631 log_k_meso_free          -2.8314  -3.1375 -2.525 log_k_meso_free_bound    -3.2213  -4.4695 -1.973 log_k_meso_bound_free    -2.4246  -3.5668 -1.282 a.1                       4.7372   3.9542  5.520 SD.meso_free_0            0.1634 -32.7769 33.104 SD.log_k_meso_free        0.4885   0.3080  0.669 SD.log_k_meso_free_bound  0.2876  -1.7955  2.371 SD.log_k_meso_bound_free  0.9942   0.2181  1.770  Correlation:                        ms_fr_0 lg_k_m_ lg_k_ms_f_ log_k_meso_free        0.2332                    log_k_meso_free_bound  0.1100  0.5964            log_k_meso_bound_free -0.0413  0.3697  0.8025     Random effects:                            est.    lower  upper SD.meso_free_0           0.1634 -32.7769 33.104 SD.log_k_meso_free       0.4885   0.3080  0.669 SD.log_k_meso_free_bound 0.2876  -1.7955  2.371 SD.log_k_meso_bound_free 0.9942   0.2181  1.770  Variance model:      est. lower upper a.1 4.737 3.954  5.52  Backtransformed parameters:                       est.    lower    upper meso_free_0       93.62849 91.62622 95.63075 k_meso_free        0.05893  0.04339  0.08004 k_meso_free_bound  0.03990  0.01145  0.13903 k_meso_bound_free  0.08851  0.02825  0.27736  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.15333 0.03402 0.20881   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 14.79 60.81     18.3        4.521        20.37 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:03 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.61 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   781.9 789.9   -382  Optimised parameters:               est.    lower   upper meso_0    93.34242  91.4730 95.2118 log_k1    -2.77312  -3.0826 -2.4637 log_k2    -3.61854  -3.8430 -3.3941 log_tb     2.00266   1.3357  2.6696 a.1        4.47693   3.7059  5.2479 SD.meso_0  0.07963 -63.1661 63.3253 SD.log_k1  0.47817   0.2467  0.7097 SD.log_k2  0.39216   0.2137  0.5706 SD.log_tb  0.94683   0.4208  1.4728  Correlation:         meso_0  log_k1  log_k2  log_k1  0.1627                 log_k2  0.0063 -0.0301         log_tb  0.0083 -0.3931 -0.1225  Random effects:              est.    lower   upper SD.meso_0 0.07963 -63.1661 63.3253 SD.log_k1 0.47817   0.2467  0.7097 SD.log_k2 0.39216   0.2137  0.5706 SD.log_tb 0.94683   0.4208  1.4728  Variance model:      est. lower upper a.1 4.477 3.706 5.248  Backtransformed parameters:            est.    lower    upper meso_0 93.34242 91.47303 95.21181 k1      0.06247  0.04584  0.08512 k2      0.02682  0.02143  0.03357 tb      7.40872  3.80282 14.43376  Estimated disappearance times:      DT50 DT90 DT50back DT50_k1 DT50_k2 meso   16   76    22.88    11.1   25.84"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"fits-with-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits with covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchichal SFO fit pH influence  Hierarchichal FOMC fit pH influence  Refined hierarchichal FOMC fit pH influence  Hierarchichal DFOP fit pH influence  Refined hierarchical DFOP fit pH influence  refined hierarchical DFOP fit pH influence  Hierarchichal SFORB fit pH influence  Refined hierarchichal SFORB fit pH influence  Hierarchichal HS fit pH influence  Refined hierarchichal HS fit pH influence","code":"saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:15 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.924 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   783.1 787.5 -386.5  Optimised parameters:                        est.   lower   upper meso_0              91.3481 89.2688 93.4275 log_k_meso          -3.2854 -3.4590 -3.1118 beta_pH(log_k_meso)  0.5871  0.3684  0.8059 a.1                  5.4750  4.7085  6.2415 SD.log_k_meso        0.3471  0.2258  0.4684  Correlation:                      meso_0  lg_k_ms log_k_meso           0.1797         beta_pH(log_k_meso) -0.0183 -0.2379  Random effects:                 est.  lower  upper SD.log_k_meso 0.3471 0.2258 0.4684  Variance model:      est. lower upper a.1 5.475 4.709 6.242  Backtransformed parameters:            est.    lower    upper meso_0 91.34814 89.26880 93.42748 k_meso  0.03743  0.03146  0.04452  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 meso 18.52 61.52 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:18 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.901 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.1 776.3   -378  Optimised parameters:                         est.      lower   upper meso_0             92.840646  90.750461 94.9308 log_alpha           1.114051   0.475668  1.7524 beta_pH(log_alpha)  0.577505   0.369805  0.7852 log_beta            4.214099   3.438851  4.9893 a.1                 5.027768   4.322028  5.7335 SD.log_alpha        0.004034 -23.766993 23.7751 SD.log_beta         0.374640   0.009252  0.7400  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.3220                 beta_pH(log_alpha) -0.0789  0.1148         log_beta           -0.3544  0.9709  0.1628  Random effects:                  est.      lower upper SD.log_alpha 0.004034 -23.766993 23.78 SD.log_beta  0.374640   0.009252  0.74  Variance model:      est. lower upper a.1 5.028 4.322 5.734  Backtransformed parameters:          est.  lower   upper meso_0 92.841 90.750  94.931 alpha   3.047  1.609   5.769 beta   67.633 31.151 146.840  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back meso 17.28 76.37    22.99 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:21 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.35 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   766.5 771.8 -377.3  Optimised parameters:                       est.   lower   upper meso_0             93.1950 91.1047 95.2853 log_alpha           0.8125  0.3292  1.2957 beta_pH(log_alpha)  0.5497  0.3490  0.7504 log_beta            3.8464  3.2179  4.4750 a.1                 4.9972  4.2976  5.6968 SD.log_beta         0.3829  0.2499  0.5159  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.3094                 beta_pH(log_alpha) -0.0779  0.0841         log_beta           -0.3493  0.9533  0.1434  Random effects:               est.  lower  upper SD.log_beta 0.3829 0.2499 0.5159  Variance model:      est. lower upper a.1 4.997 4.298 5.697  Backtransformed parameters:          est. lower  upper meso_0 93.195 91.10 95.285 alpha   2.253  1.39  3.654 beta   46.826 24.98 87.793  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back meso 16.86 83.27    25.07 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:25 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.592 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.1 777.1 -375.5  Optimised parameters:                        est.   lower    upper meso_0            92.843344 90.8464 94.84028 log_k1            -2.815685 -3.0888 -2.54261 log_k2            -3.956384 -4.4741 -3.43868 beta_pH(log_k2)    1.308417  0.6948  1.92203 g_qlogis          -0.121394 -0.5691  0.32627 beta_pH(g_qlogis) -0.565988 -1.0394 -0.09262 a.1                4.955518  4.2597  5.65135 SD.log_k2          0.758963  0.4685  1.04943 SD.g_qlogis        0.005215 -9.9561  9.96656  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.2706                                 log_k2            -0.0457 -0.0667                         beta_pH(log_k2)    0.0554 -0.1291 -0.5566                 g_qlogis           0.1004 -0.4462 -0.4397  0.5042         beta_pH(g_qlogis)  0.1267  0.4226  0.0123 -0.0438  0.2029  Random effects:                 est.   lower upper SD.log_k2   0.758963  0.4685 1.049 SD.g_qlogis 0.005215 -9.9561 9.967  Variance model:      est. lower upper a.1 4.956  4.26 5.651  Backtransformed parameters:            est.    lower    upper meso_0 92.84334 90.84641 94.84028 k1      0.05986  0.04556  0.07866 k2      0.01913  0.01140  0.03211 g       0.46969  0.36145  0.58085  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 20.23 88.45    26.62   11.58   36.23 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:30 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 4.483 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   765.1 772.2 -374.5  Optimised parameters:                      est.   lower    upper meso_0            93.1612 91.0766 95.24580 log_k1            -1.8857 -2.8975 -0.87395 log_k2            -3.5711 -3.8859 -3.25622 beta_pH(log_k2)    0.8252  0.4952  1.15513 g_qlogis          -1.5326 -2.6994 -0.36574 beta_pH(g_qlogis) -0.8365 -1.7163  0.04333 a.1                4.9218  4.2328  5.61070 SD.log_k2          0.4011  0.2604  0.54184  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.1734                                 log_k2             0.0553  0.6421                         beta_pH(log_k2)   -0.0382 -0.5121 -0.5860                 g_qlogis           0.0616 -0.8501 -0.7251  0.4941         beta_pH(g_qlogis)  0.1408 -0.3240 -0.2716 -0.0041  0.6264  Random effects:             est.  lower  upper SD.log_k2 0.4011 0.2604 0.5418  Variance model:      est. lower upper a.1 4.922 4.233 5.611  Backtransformed parameters:            est.    lower    upper meso_0 93.16118 91.07655 95.24580 k1      0.15172  0.05516  0.41730 k2      0.02813  0.02053  0.03853 g       0.17762  0.06301  0.40957  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 18.47 74.92    22.55   4.569   24.64 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:37 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.213 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   767.4 773.6 -376.7  Optimised parameters:                    est.   lower   upper meso_0          93.3011 91.1905 95.4118 log_k1          -2.1487 -2.7607 -1.5367 log_k2          -3.6066 -3.9305 -3.2828 beta_pH(log_k2)  0.7821  0.4126  1.1517 g_qlogis        -1.0373 -1.9337 -0.1409 a.1              5.0095  4.3082  5.7108 SD.log_k2        0.4622  0.3009  0.6235  Correlation:                  meso_0  log_k1  log_k2  b_H(_2) log_k1           0.2179                         log_k2           0.0271  0.5067                 beta_pH(log_k2) -0.0326 -0.5546 -0.5485         g_qlogis         0.0237 -0.8479 -0.6866  0.6123  Random effects:             est.  lower  upper SD.log_k2 0.4622 0.3009 0.6235  Variance model:      est. lower upper a.1 5.009 4.308 5.711  Backtransformed parameters:            est.    lower    upper meso_0 93.30113 91.19050 95.41175 k1      0.11664  0.06325  0.21508 k2      0.02714  0.01963  0.03752 g       0.26168  0.12635  0.46483  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 17.09 73.67    22.18   5.943   25.54 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:41 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.758 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   768.8 776.8 -375.4  Optimised parameters:                                   est.   lower   upper meso_free_0                    93.4204 91.3213 95.5195 log_k_meso_free                -2.9408 -3.2344 -2.6471 beta_pH(log_k_meso_free)        0.4232  0.1769  0.6695 log_k_meso_free_bound          -3.4889 -4.9243 -2.0535 log_k_meso_bound_free          -2.9130 -4.3047 -1.5212 beta_pH(log_k_meso_bound_free)  1.2290 -0.2107  2.6687 a.1                             4.9031  4.1795  5.6268 SD.log_k_meso_free              0.3454  0.2252  0.4656 SD.log_k_meso_bound_free        0.1277 -1.9459  2.2012  Correlation:                                 ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_ log_k_meso_free                 0.3460                                        beta_pH(log_k_meso_free)       -0.0930 -0.4206                                log_k_meso_free_bound           0.2439  0.7749 -0.3492                        log_k_meso_bound_free           0.1350  0.6400 -0.2912   0.9346               beta_pH(log_k_meso_bound_free) -0.2216 -0.4778 -0.0111  -0.6566    -0.6498     Random effects:                            est.   lower  upper SD.log_k_meso_free       0.3454  0.2252 0.4656 SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012  Variance model:      est. lower upper a.1 4.903  4.18 5.627  Backtransformed parameters:                       est.     lower    upper meso_free_0       93.42040 91.321340 95.51946 k_meso_free        0.05282  0.039384  0.07085 k_meso_free_bound  0.03054  0.007268  0.12829 k_meso_bound_free  0.05431  0.013505  0.21845  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g   0.1121  0.0256  0.3148   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.42 75.2    22.64        6.185        27.08 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:44 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.971 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.9 777.2 -378.5  Optimised parameters:                             est.   lower   upper meso_free_0              93.3196 91.1650 95.4743 log_k_meso_free          -3.0207 -3.2655 -2.7759 beta_pH(log_k_meso_free)  0.5435  0.3329  0.7542 log_k_meso_free_bound    -3.8001 -5.1809 -2.4193 log_k_meso_bound_free    -2.9462 -4.2411 -1.6513 a.1                       5.0825  4.3709  5.7940 SD.log_k_meso_free        0.3338  0.2175  0.4502  Correlation:                           ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_ log_k_meso_free           0.3556                            beta_pH(log_k_meso_free) -0.0422 -0.2041                    log_k_meso_free_bound     0.2513  0.6866 -0.0400            log_k_meso_bound_free     0.1292  0.5341 -0.0129  0.9214     Random effects:                      est.  lower  upper SD.log_k_meso_free 0.3338 0.2175 0.4502  Variance model:      est. lower upper a.1 5.082 4.371 5.794  Backtransformed parameters:                       est.     lower    upper meso_free_0       93.31965 91.165044 95.47425 k_meso_free        0.04877  0.038177  0.06230 k_meso_free_bound  0.02237  0.005623  0.08898 k_meso_bound_free  0.05254  0.014392  0.19179  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.09736 0.02632 0.31602   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.87 73.16    22.02         7.12        26.34 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:48 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.254 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.8 779.6 -373.9  Optimised parameters:                     est.   lower   upper meso_0          93.32599 91.4658 95.1862 log_k1          -3.08497 -3.2830 -2.8870 beta_pH(log_k1)  0.47472  0.2334  0.7160 log_k2          -3.68267 -3.9077 -3.4577 beta_pH(log_k2)  0.54151  0.2124  0.8706 log_tb           2.67815  2.3846  2.9717 beta_pH(log_tb) -0.09889 -0.4258  0.2280 a.1              4.49487  3.7766  5.2132 SD.log_k1        0.37191  0.2370  0.5068 SD.log_k2        0.29210  0.0994  0.4848 SD.log_tb        0.25353 -0.0664  0.5735  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_tb  log_k1           0.2301                                         beta_pH(log_k1) -0.0452 -0.2842                                 log_k2          -0.0029 -0.0322 -0.0008                         beta_pH(log_k2) -0.0071  0.0000 -0.0391 -0.2655                 log_tb          -0.1003 -0.2052  0.1180 -0.4197  0.1794         beta_pH(log_tb)  0.0097  0.1125 -0.1265  0.1894 -0.3653 -0.3449  Random effects:             est.   lower  upper SD.log_k1 0.3719  0.2370 0.5068 SD.log_k2 0.2921  0.0994 0.4848 SD.log_tb 0.2535 -0.0664 0.5735  Variance model:      est. lower upper a.1 4.495 3.777 5.213  Backtransformed parameters:            est.    lower    upper meso_0 93.32599 91.46575 95.18624 k1      0.04573  0.03752  0.05574 k2      0.02516  0.02009  0.03150 tb     14.55810 10.85502 19.52445  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 15.65 79.63    23.97   15.16   27.55 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:50 2025  Date of summary: Fri Sep 12 22:15:50 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.425 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   766.5 775.4 -373.2  Optimised parameters:                    est.    lower   upper meso_0          93.3251 91.49823 95.1520 log_k1          -3.0535 -3.24879 -2.8582 beta_pH(log_k1)  0.4567  0.22400  0.6894 log_k2          -3.7439 -3.94307 -3.5447 beta_pH(log_k2)  0.4982  0.20644  0.7899 log_tb           2.7040  2.33033  3.0777 a.1              4.4452  3.73537  5.1551 SD.log_k1        0.3570  0.22104  0.4930 SD.log_k2        0.2252  0.01864  0.4318 SD.log_tb        0.5488  0.24560  0.8521  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_k1           0.2233                                 beta_pH(log_k1) -0.0453 -0.2955                         log_k2           0.0028 -0.0420  0.0126                 beta_pH(log_k2) -0.0116  0.0112 -0.0667 -0.2097         log_tb          -0.0658 -0.1928  0.0913 -0.2843  0.1210  Random effects:             est.   lower  upper SD.log_k1 0.3570 0.22104 0.4930 SD.log_k2 0.2252 0.01864 0.4318 SD.log_tb 0.5488 0.24560 0.8521  Variance model:      est. lower upper a.1 4.445 3.735 5.155  Backtransformed parameters:            est.    lower    upper meso_0 93.32513 91.49823 95.15204 k1      0.04719  0.03882  0.05737 k2      0.02366  0.01939  0.02888 tb     14.93925 10.28132 21.70744  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 14.69 82.45    24.82   14.69   29.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Session info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"R version 4.5.1 (2025-06-13) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 13 (trixie)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.1  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.1;  LAPACK version 3.12.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.4      npde_3.5        [5] knitr_1.50      mkin_1.2.11      loaded via a namespace (and not attached):  [1] gtable_0.3.6       jsonlite_2.0.0     dplyr_1.1.4        compiler_4.5.1      [5] tidyselect_1.2.1   dichromat_2.0-0.1  gridExtra_2.3      jquerylib_0.1.4     [9] systemfonts_1.2.3  scales_1.4.0       textshaping_1.0.1  readxl_1.4.5       [13] yaml_2.3.10        fastmap_1.2.0      lattice_0.22-7     ggplot2_3.5.2      [17] R6_2.6.1           generics_0.1.4     lmtest_0.9-40      MASS_7.3-65        [21] htmlwidgets_1.6.4  tibble_3.3.0       desc_1.4.3         bslib_0.9.0        [25] pillar_1.10.2      RColorBrewer_1.1-3 rlang_1.1.6        cachem_1.1.0       [29] xfun_0.52          fs_1.6.6           sass_0.4.10        cli_3.6.5          [33] pkgdown_2.1.3      magrittr_2.0.3     digest_0.6.37      grid_4.5.1         [37] mclust_6.1.1       lifecycle_1.0.4    nlme_3.1-168       vctrs_0.6.5        [41] evaluate_1.0.3     glue_1.8.0         cellranger_1.1.0   farver_2.1.2       [45] codetools_0.2-20   ragg_1.4.0         zoo_1.8-14         colorspace_2.1-1   [49] tools_4.5.1        pkgconfig_2.0.3    htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hardware-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Hardware info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64933716 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package, also considering influence covariates like soil pH different degradation parameters. case studies, SFORB parameterisation biexponential decline shown advantages DFOP parameterisation, SFORB included list tested models well. mkin package used version 1.2.10, contains functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"Introduction","what":"Test data","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following tables show covariate data 18 datasets read spreadsheet file. Covariate data Dataset Richmond Dataset Richmond 2 Dataset ERTC Dataset Toulouse Dataset Picket Piece Dataset 721 Dataset 722 Dataset 723 Dataset 724 Dataset 725 Dataset 727 Dataset 728 Dataset 729 Dataset 730 Dataset 731 Dataset 732 Dataset 741 Dataset 742","code":"data_file <- system.file(   \"testdata\", \"mesotrione_soil_efsa_2016.xlsx\", package = \"mkin\") meso_ds <- read_spreadsheet(data_file, parent_only = TRUE) pH <- attr(meso_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(meso_ds)) {   print(     kable(mkin_long_to_wide(meso_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE)) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"","what":"Separate evaluations","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"order obtain suitable starting parameters NLHM fits, separate fits five models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. tables , OK indicates convergence C indicates failure converge. separate fits constant variance converged, exception two FOMC fits, one SFORB fit one HS fit. two-component error model, set fits converge larger, convergence problems appearing number non-SFO fits.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\") f_sep_const <- mmkin(   deg_mods,   meso_ds,   error_model = \"const\",   cluster = cl,   quiet = TRUE) status(f_sep_const[, 1:5]) |> kable() status(f_sep_const[, 6:18]) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc[, 1:5]) |> kable() status(f_sep_tc[, 6:18]) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hierarchical-models-without-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models without covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following code fits hierarchical kinetic models ten combinations five different degradation models two different error models parallel. fits terminate without errors (status OK). model comparisons show fits constant variance consistently preferable corresponding fits two-component error data. confirmed fact parameter b.1 (relative standard deviation fits obtained saemix package), ill-defined fits. obtaining fits well-defined random effects, update set fits, excluding random effects ill-defined according illparms function. updated fits terminate without errors. ill-defined errors remain fits constant variance.","code":"f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) status(f_saem_1) |> kable() anova(f_saem_1) |> kable(digits = 1) illparms(f_saem_1) |> kable() f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hierarchical-models-with-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models with covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following sections, hierarchical fits including model influence pH selected degradation parameters shown parent models. Constant variance selected error model based fits without covariate effects. Random effects ill-defined fits without pH influence excluded. potential influence soil pH included parameters well-defined random effect, experience shown parameters significant pH effect found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"sfo","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFO","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"parameter showing pH influence table beta_pH(log_k_meso). confidence interval include zero, indicating influence soil pH log degradation rate constant significantly greater zero. comparison SFO fit without covariate effect confirms considering soil pH improves model, comparison AIC BIC likelihood ratio test.  Endpoints model covariates default calculated median covariate values. quantile can adapted, specific covariate value can given shown .","code":"sfo_pH <- saem(f_sep_const[\"SFO\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_k_meso ~ pH)) summary(sfo_pH)$confint_trans |> kable(digits = 2) anova(f_saem_2[[\"SFO\", \"const\"]], sfo_pH, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"SFO\", \"const\"]]    4 797.56 801.12 -394.78                          sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(sfo_pH) endpoints(sfo_pH) $covariates       pH 50% 5.75  $distimes          DT50     DT90 meso 18.52069 61.52441 endpoints(sfo_pH, covariate_quantile = 0.9) $covariates       pH 90% 7.13  $distimes          DT50     DT90 meso 8.237019 27.36278 endpoints(sfo_pH, covariates = c(pH = 7.0)) $covariates      pH User  7  $distimes         DT50    DT90 meso 8.89035 29.5331"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"fomc","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"FOMC","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"case SFO, confidence interval slope parameter (beta_pH(log_alpha)) quantifying influence soil pH include zero, model comparison clearly indicates model covariate influence preferable. However, random effect alpha well-defined inclusion covariate effect (confidence interval SD.log_alpha includes zero). Therefore, model updated without random effect, ill-defined parameters remain. Model comparison indicates including pH dependence significantly improves fit, reduced model covariate influence results preferable FOMC fit.","code":"fomc_pH <- saem(f_sep_const[\"FOMC\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_alpha ~ pH)) summary(fomc_pH)$confint_trans |> kable(digits = 2) illparms(fomc_pH) [1] \"sd(log_alpha)\" fomc_pH_2 <- update(fomc_pH, no_random_effect = c(\"meso_0\", \"log_alpha\")) illparms(fomc_pH_2) anova(f_saem_2[[\"FOMC\", \"const\"]], fomc_pH, fomc_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"FOMC\", \"const\"]]    5 783.25 787.71 -386.63                          fomc_pH_2                      6 767.49 772.83 -377.75 17.762  1  2.503e-05 *** fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(fomc_pH_2)$confint_trans |> kable(digits = 2) plot(fomc_pH_2) endpoints(fomc_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back meso 17.30248 82.91343 24.95943 endpoints(fomc_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back meso 6.986239 27.02927 8.136621"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"dfop","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"DFOP","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"DFOP fits without covariate effects, random effects two degradation parameters (k2 g) identifiable. fit pH dependent degradation parameters obtained excluding random effects refined DFOP fit without covariate influence, including covariate models two identifiable parameters k2 g. corresponding parameters influence soil pH beta_pH(log_k2) influence soil pH k2, beta_pH(g_qlogis) influence g. Confidence intervals neither include zero, indicating significant difference zero. However, random effect g now ill-defined. fit updated without ill-defined random effect. Now, slope parameter pH effect g ill-defined. Therefore, another attempt made without corresponding covariate model. random effect g ill-defined, fit repeated without . ill-defined parameters remain, model comparison suggests previous model dfop_pH_2 two pH dependent parameters preferable, based information criteria well based likelihood ratio test. focussing parameter identifiability using test confidence interval includes zero, dfop_pH_4 still preferred model. However, kept mind parameter confidence intervals constructed using simple linearisation likelihood. confidence interval random effect g marginally includes zero, suggested acceptable, dfop_pH_2 can considered preferable model.","code":"summary(f_saem_2[[\"DFOP\", \"const\"]])$confint_trans |> kable(digits = 2) dfop_pH <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH)) summary(dfop_pH)$confint_trans |> kable(digits = 2) illparms(dfop_pH) [1] \"sd(g_qlogis)\" dfop_pH_2 <- update(dfop_pH,   no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_2) [1] \"beta_pH(g_qlogis)\" dfop_pH_3 <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH)) illparms(dfop_pH_3) [1] \"sd(g_qlogis)\" dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_4) anova(f_saem_2[[\"DFOP\", \"const\"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik f_saem_2[[\"DFOP\", \"const\"]]    7 782.94 789.18 -384.47 dfop_pH_4                      7 767.35 773.58 -376.68 dfop_pH_2                      8 765.14 772.26 -374.57 dfop_pH_3                      8 769.00 776.12 -376.50 dfop_pH                        9 769.10 777.11 -375.55 anova(dfop_pH_2, dfop_pH_4, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)   dfop_pH_4    7 767.35 773.58 -376.68                        dfop_pH_2    8 765.14 772.26 -374.57 4.2153  1    0.04006 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(dfop_pH_2) endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"sforb","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFORB","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"confidence interval beta_pH(log_k_meso_bound_free) includes zero, indicating influence soil pH k_meso_bound_free reliably quantified. Also, confidence interval random effect parameter (SD.log_k_meso_bound_free) includes zero. Using illparms function, ill-defined parameters can found conveniently. remove ill-defined parameters, second variant SFORB model pH influence fitted. ill-defined parameters remain. model comparison SFORB fits includes refined model without covariate effect, versions SFORB fit covariate effect. first model including pH influence preferable based information criteria likelihood ratio test. However, fully identifiable, second model selected.","code":"sforb_pH <- saem(f_sep_const[\"SFORB\", ], no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\"),   covariates = pH,   covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH)) summary(sforb_pH)$confint_trans |> kable(digits = 2) illparms(sforb_pH) [1] \"sd(log_k_meso_bound_free)\"      \"beta_pH(log_k_meso_bound_free)\" sforb_pH_2 <- update(sforb_pH,   no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\", \"log_k_meso_bound_free\"),   covariate_models = list(log_k_meso_free ~ pH)) illparms(sforb_pH_2) anova(f_saem_2[[\"SFORB\", \"const\"]], sforb_pH, sforb_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                               npar    AIC    BIC     Lik   Chisq Df Pr(>Chisq)   f_saem_2[[\"SFORB\", \"const\"]]    7 783.40 789.63 -384.70                         sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0              sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(sforb_pH_2)$confint_trans |> kable(digits = 2) plot(sforb_pH_2) endpoints(sforb_pH_2) $covariates       pH 50% 5.75  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.09735824 0.02631699 0.31602120   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.86549 73.15824 22.02282     7.119554     26.33839 endpoints(sforb_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.13315233 0.03795988 0.61186191   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 7.932495 36.93311 11.11797     5.205671        18.26"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hs","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"HS","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"According output illparms function, random effect break time tb reliably quantified, neither can influence soil pH tb. fit repeated without corresponding covariate model, ill-defined parameters remain. Model comparison confirms model preferable fit without covariate influence, also first version covariate influence.","code":"hs_pH <- saem(f_sep_const[\"HS\", ], no_random_effect = c(\"meso_0\"),   covariates = pH,   covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH)) summary(hs_pH)$confint_trans |> kable(digits = 2) illparms(hs_pH) [1] \"sd(log_tb)\"      \"beta_pH(log_tb)\" hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) illparms(hs_pH_2) anova(f_saem_2[[\"HS\", \"const\"]], hs_pH, hs_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"HS\", \"const\"]]    8 780.08 787.20 -382.04                          hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 *** hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(hs_pH_2)$confint_trans |> kable(digits = 2) plot(hs_pH_2) endpoints(hs_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 14.68725 82.45287 24.82079 14.68725 29.29299 endpoints(hs_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.298536 38.85371 11.69613 8.298536 15.71561"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"comparison-across-parent-models","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"Comparison across parent models","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"model reduction models pH influence, compared . DFOP model pH influence k2 g random effect k2 finally selected best fit. endpoints resulting model listed . Please refer Appendix detailed listing.","code":"anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2) Data: 116 observations of 1 variable(s) grouped in 18 datasets             npar    AIC    BIC     Lik sfo_pH        5 783.09 787.54 -386.54 fomc_pH_2     6 767.49 772.83 -377.75 dfop_pH_4     7 767.35 773.58 -376.68 sforb_pH_2    7 770.94 777.17 -378.47 dfop_pH_2     8 765.14 772.26 -374.57 hs_pH_2      10 766.47 775.37 -373.23 endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"evaluations demonstrate covariate effects can included types parent degradation models. models can refined make fully identifiable.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"fits-without-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits without covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchical SFO fit constant variance  Hierarchical FOMC fit constant variance  Hierarchical DFOP fit constant variance  Hierarchical SFORB fit constant variance  Hierarchical HS fit constant variance","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.682 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling   AIC   BIC logLik   800 804.5   -395  Optimised parameters:                  est.    lower   upper meso_0        92.0705  89.9917 94.1493 log_k_meso    -3.1641  -3.4286 -2.8996 a.1            5.4628   4.6421  6.2835 SD.meso_0      0.0611 -98.3545 98.4767 SD.log_k_meso  0.5616   0.3734  0.7499  Correlation:             meso_0 log_k_meso 0.1132  Random effects:                 est.    lower   upper SD.meso_0     0.0611 -98.3545 98.4767 SD.log_k_meso 0.5616   0.3734  0.7499  Variance model:      est. lower upper a.1 5.463 4.642 6.284  Backtransformed parameters:            est.    lower    upper meso_0 92.07053 89.99172 94.14933 k_meso  0.04225  0.03243  0.05505  Estimated disappearance times:       DT50 DT90 meso 16.41 54.5 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.817 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 793.6 -386.7  Optimised parameters:                 est.     lower   upper meso_0       93.5648  91.42864 95.7009 log_alpha     0.7645   0.28068  1.2484 log_beta      3.6597   3.05999  4.2594 a.1           5.0708   4.29823  5.8435 SD.meso_0     0.1691 -34.01517 34.3535 SD.log_alpha  0.3764   0.05834  0.6945 SD.log_beta   0.3903  -0.06074  0.8414  Correlation:            meso_0  log_lph log_alpha -0.2839         log_beta  -0.3443  0.8855  Random effects:                est.     lower   upper SD.meso_0    0.1691 -34.01517 34.3535 SD.log_alpha 0.3764   0.05834  0.6945 SD.log_beta  0.3903  -0.06074  0.8414  Variance model:      est. lower upper a.1 5.071 4.298 5.843  Backtransformed parameters:          est.  lower  upper meso_0 93.565 91.429 95.701 alpha   2.148  1.324  3.485 beta   38.850 21.327 70.770  Estimated disappearance times:      DT50  DT90 DT50back meso 14.8 74.64    22.47 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.188 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.6 795.6 -384.8  Optimised parameters:                est.     lower   upper meso_0      93.6684  91.63599 95.7008 log_k1      -1.7354  -2.61433 -0.8565 log_k2      -3.4015  -3.73323 -3.0697 g_qlogis    -1.6341  -2.66133 -0.6069 a.1          4.7803   4.01269  5.5479 SD.meso_0    0.1661 -30.97086 31.3031 SD.log_k1    0.1127  -2.59680  2.8223 SD.log_k2    0.6394   0.41499  0.8638 SD.g_qlogis  0.8166   0.09785  1.5353  Correlation:           meso_0  log_k1  log_k2  log_k1    0.1757                 log_k2    0.0199  0.2990         g_qlogis  0.0813 -0.7431 -0.3826  Random effects:               est.     lower   upper SD.meso_0   0.1661 -30.97086 31.3031 SD.log_k1   0.1127  -2.59680  2.8223 SD.log_k2   0.6394   0.41499  0.8638 SD.g_qlogis 0.8166   0.09785  1.5353  Variance model:     est. lower upper a.1 4.78 4.013 5.548  Backtransformed parameters:            est.    lower    upper meso_0 93.66841 91.63599 95.70082 k1      0.17633  0.07322  0.42466 k2      0.03332  0.02392  0.04643 g       0.16327  0.06529  0.35277  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 16.04 63.75    19.19   3.931    20.8 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.223 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 795.4 -384.7  Optimised parameters:                             est.    lower  upper meso_free_0              93.6285  91.6262 95.631 log_k_meso_free          -2.8314  -3.1375 -2.525 log_k_meso_free_bound    -3.2213  -4.4695 -1.973 log_k_meso_bound_free    -2.4246  -3.5668 -1.282 a.1                       4.7372   3.9542  5.520 SD.meso_free_0            0.1634 -32.7769 33.104 SD.log_k_meso_free        0.4885   0.3080  0.669 SD.log_k_meso_free_bound  0.2876  -1.7955  2.371 SD.log_k_meso_bound_free  0.9942   0.2181  1.770  Correlation:                        ms_fr_0 lg_k_m_ lg_k_ms_f_ log_k_meso_free        0.2332                    log_k_meso_free_bound  0.1100  0.5964            log_k_meso_bound_free -0.0413  0.3697  0.8025     Random effects:                            est.    lower  upper SD.meso_free_0           0.1634 -32.7769 33.104 SD.log_k_meso_free       0.4885   0.3080  0.669 SD.log_k_meso_free_bound 0.2876  -1.7955  2.371 SD.log_k_meso_bound_free 0.9942   0.2181  1.770  Variance model:      est. lower upper a.1 4.737 3.954  5.52  Backtransformed parameters:                       est.    lower    upper meso_free_0       93.62849 91.62622 95.63075 k_meso_free        0.05893  0.04339  0.08004 k_meso_free_bound  0.03990  0.01145  0.13903 k_meso_bound_free  0.08851  0.02825  0.27736  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.15333 0.03402 0.20881   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 14.79 60.81     18.3        4.521        20.37 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:36 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.307 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   781.9 789.9   -382  Optimised parameters:               est.    lower   upper meso_0    93.34242  91.4730 95.2118 log_k1    -2.77312  -3.0826 -2.4637 log_k2    -3.61854  -3.8430 -3.3941 log_tb     2.00266   1.3357  2.6696 a.1        4.47693   3.7059  5.2479 SD.meso_0  0.07963 -63.1661 63.3253 SD.log_k1  0.47817   0.2467  0.7097 SD.log_k2  0.39216   0.2137  0.5706 SD.log_tb  0.94683   0.4208  1.4728  Correlation:         meso_0  log_k1  log_k2  log_k1  0.1627                 log_k2  0.0063 -0.0301         log_tb  0.0083 -0.3931 -0.1225  Random effects:              est.    lower   upper SD.meso_0 0.07963 -63.1661 63.3253 SD.log_k1 0.47817   0.2467  0.7097 SD.log_k2 0.39216   0.2137  0.5706 SD.log_tb 0.94683   0.4208  1.4728  Variance model:      est. lower upper a.1 4.477 3.706 5.248  Backtransformed parameters:            est.    lower    upper meso_0 93.34242 91.47303 95.21181 k1      0.06247  0.04584  0.08512 k2      0.02682  0.02143  0.03357 tb      7.40872  3.80282 14.43376  Estimated disappearance times:      DT50 DT90 DT50back DT50_k1 DT50_k2 meso   16   76    22.88    11.1   25.84"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"fits-with-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits with covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchichal SFO fit pH influence  Hierarchichal FOMC fit pH influence  Refined hierarchichal FOMC fit pH influence  Hierarchichal DFOP fit pH influence  Refined hierarchical DFOP fit pH influence  refined hierarchical DFOP fit pH influence  Hierarchichal SFORB fit pH influence  Refined hierarchichal SFORB fit pH influence  Hierarchichal HS fit pH influence  Refined hierarchichal HS fit pH influence","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:49 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.739 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   783.1 787.5 -386.5  Optimised parameters:                        est.   lower   upper meso_0              91.3481 89.2688 93.4275 log_k_meso          -6.6614 -7.9715 -5.3514 beta_pH(log_k_meso)  0.5871  0.3684  0.8059 a.1                  5.4750  4.7085  6.2415 SD.log_k_meso        0.3471  0.2258  0.4684  Correlation:                      meso_0  lg_k_ms log_k_meso           0.0414         beta_pH(log_k_meso) -0.0183 -0.9917  Random effects:                 est.  lower  upper SD.log_k_meso 0.3471 0.2258 0.4684  Variance model:      est. lower upper a.1 5.475 4.709 6.242  Backtransformed parameters:             est.     lower     upper meso_0 91.348139 8.927e+01 93.427476 k_meso  0.001279 3.452e-04  0.004741  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 meso 18.52 61.52 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:51 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.076 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.1 776.3   -378  Optimised parameters:                         est.      lower   upper meso_0             92.840646  90.750461 94.9308 log_alpha          -2.206602  -3.494546 -0.9187 beta_pH(log_alpha)  0.577505   0.369805  0.7852 log_beta            4.214099   3.438851  4.9893 a.1                 5.027768   4.322028  5.7335 SD.log_alpha        0.004034 -23.766993 23.7751 SD.log_beta         0.374640   0.009252  0.7400  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.0865                 beta_pH(log_alpha) -0.0789 -0.8704         log_beta           -0.3544  0.3302  0.1628  Random effects:                  est.      lower upper SD.log_alpha 0.004034 -23.766993 23.78 SD.log_beta  0.374640   0.009252  0.74  Variance model:      est. lower upper a.1 5.028 4.322 5.734  Backtransformed parameters:           est.    lower    upper meso_0 92.8406 90.75046  94.9308 alpha   0.1101  0.03036   0.3991 beta   67.6332 31.15113 146.8404  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back meso 17.28 76.37    22.99 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:55 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.361 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   767.5 772.8 -377.7  Optimised parameters:                       est.   lower   upper meso_0             93.0536 90.9771 95.1300 log_alpha          -2.9054 -4.1803 -1.6304 beta_pH(log_alpha)  0.6590  0.4437  0.8744 log_beta            3.9549  3.2860  4.6239 a.1                 4.9784  4.2815  5.6754 SD.log_beta         0.4019  0.2632  0.5406  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.0397                 beta_pH(log_alpha) -0.0899 -0.9146         log_beta           -0.3473  0.2038  0.1919  Random effects:               est.  lower  upper SD.log_beta 0.4019 0.2632 0.5406  Variance model:      est. lower upper a.1 4.978 4.281 5.675  Backtransformed parameters:            est.    lower    upper meso_0 93.05359 90.97713  95.1300 alpha   0.05473  0.01529   0.1958 beta   52.19251 26.73597 101.8874  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:      DT50  DT90 DT50back meso 17.3 82.91    24.96 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:58 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.758 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.1 777.1 -375.5  Optimised parameters:                         est.    lower    upper meso_0             92.843344  90.8464 94.84028 log_k1             -2.815685  -3.0888 -2.54261 log_k2            -11.479779 -15.3203 -7.63923 beta_pH(log_k2)     1.308417   0.6948  1.92203 g_qlogis            3.133036   0.4657  5.80035 beta_pH(g_qlogis)  -0.565988  -1.0394 -0.09262 a.1                 4.955518   4.2597  5.65135 SD.log_k2           0.758963   0.4685  1.04943 SD.g_qlogis         0.005215  -9.9561  9.96656  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.2706                                 log_k2            -0.0571  0.1096                         beta_pH(log_k2)    0.0554 -0.1291 -0.9937                 g_qlogis          -0.1125 -0.5062 -0.1305  0.1294         beta_pH(g_qlogis)  0.1267  0.4226  0.0419 -0.0438 -0.9864  Random effects:                 est.   lower upper SD.log_k2   0.758963  0.4685 1.049 SD.g_qlogis 0.005215 -9.9561 9.967  Variance model:      est. lower upper a.1 4.956  4.26 5.651  Backtransformed parameters:             est.     lower     upper meso_0 9.284e+01 9.085e+01 9.484e+01 k1     5.986e-02 4.556e-02 7.866e-02 k2     1.034e-05 2.221e-07 4.812e-04 g      9.582e-01 6.144e-01 9.970e-01  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 20.23 88.45    26.62   11.58   36.23 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:03 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 4.465 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   765.1 772.3 -374.6  Optimised parameters:                      est.    lower    upper meso_0            93.3333  91.2427 95.42394 log_k1            -1.7997  -2.9124 -0.68698 log_k2            -8.1810 -10.1819 -6.18008 beta_pH(log_k2)    0.8064   0.4903  1.12257 g_qlogis           3.3513  -1.1792  7.88182 beta_pH(g_qlogis) -0.8672  -1.7661  0.03177 a.1                4.9158   4.2277  5.60390 SD.log_k2          0.3946   0.2565  0.53281  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.1730                                 log_k2             0.0442  0.5370                         beta_pH(log_k2)   -0.0392 -0.4880 -0.9923                 g_qlogis          -0.1536  0.1431 -0.1129  0.1432         beta_pH(g_qlogis)  0.1504 -0.3151 -0.0196 -0.0212 -0.9798  Random effects:             est.  lower  upper SD.log_k2 0.3946 0.2565 0.5328  Variance model:      est. lower upper a.1 4.916 4.228 5.604  Backtransformed parameters:             est.     lower    upper meso_0 9.333e+01 9.124e+01 95.42394 k1     1.654e-01 5.435e-02  0.50309 k2     2.799e-04 3.785e-05  0.00207 g      9.661e-01 2.352e-01  0.99962  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 18.37 73.52    22.13   4.192   23.99 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:10 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.781 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   767.4 773.6 -376.7  Optimised parameters:                    est.    lower   upper meso_0          93.3011  91.1905 95.4118 log_k1          -2.1487  -2.7607 -1.5367 log_k2          -8.1039 -10.4225 -5.7853 beta_pH(log_k2)  0.7821   0.4126  1.1517 g_qlogis        -1.0373  -1.9337 -0.1409 a.1              5.0095   4.3082  5.7108 SD.log_k2        0.4622   0.3009  0.6235  Correlation:                  meso_0  log_k1  log_k2  b_H(_2) log_k1           0.2179                         log_k2           0.0337  0.5791                 beta_pH(log_k2) -0.0326 -0.5546 -0.9932         g_qlogis         0.0237 -0.8479 -0.6571  0.6123  Random effects:             est.  lower  upper SD.log_k2 0.4622 0.3009 0.6235  Variance model:      est. lower upper a.1 5.009 4.308 5.711  Backtransformed parameters:             est.     lower     upper meso_0 9.330e+01 9.119e+01 95.411751 k1     1.166e-01 6.325e-02  0.215084 k2     3.024e-04 2.975e-05  0.003072 g      2.617e-01 1.263e-01  0.464832  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 17.09 73.67    22.18   5.943   25.54 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:14 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.54 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   768.8 776.8 -375.4  Optimised parameters:                                   est.    lower   upper meso_free_0                    93.4204  91.3213 95.5195 log_k_meso_free                -5.3742  -6.9366 -3.8117 beta_pH(log_k_meso_free)        0.4232   0.1769  0.6695 log_k_meso_free_bound          -3.4889  -4.9243 -2.0535 log_k_meso_bound_free          -9.9797 -19.2232 -0.7362 beta_pH(log_k_meso_bound_free)  1.2290  -0.2107  2.6687 a.1                             4.9031   4.1795  5.6268 SD.log_k_meso_free              0.3454   0.2252  0.4656 SD.log_k_meso_bound_free        0.1277  -1.9459  2.2012  Correlation:                                 ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_ log_k_meso_free                 0.1493                                        beta_pH(log_k_meso_free)       -0.0930 -0.9854                                log_k_meso_free_bound           0.2439  0.4621 -0.3492                        log_k_meso_bound_free           0.2188  0.1292 -0.0339   0.7287               beta_pH(log_k_meso_bound_free) -0.2216 -0.0797 -0.0111  -0.6566    -0.9934     Random effects:                            est.   lower  upper SD.log_k_meso_free       0.3454  0.2252 0.4656 SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012  Variance model:      est. lower upper a.1 4.903  4.18 5.627  Backtransformed parameters:                        est.     lower    upper meso_free_0       9.342e+01 9.132e+01 95.51946 k_meso_free       4.635e-03 9.716e-04  0.02211 k_meso_free_bound 3.054e-02 7.268e-03  0.12829 k_meso_bound_free 4.633e-05 4.482e-09  0.47894  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g   0.1121  0.0256  0.3148   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.42 75.2    22.64        6.185        27.08 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:18 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.815 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.9 777.2 -378.5  Optimised parameters:                             est.   lower   upper meso_free_0              93.3196 91.1633 95.4760 log_k_meso_free          -6.1460 -7.4306 -4.8614 beta_pH(log_k_meso_free)  0.5435  0.3329  0.7542 log_k_meso_free_bound    -3.8001 -5.2027 -2.3975 log_k_meso_bound_free    -2.9462 -4.2565 -1.6359 a.1                       5.0825  4.3793  5.7856 SD.log_k_meso_free        0.3338  0.2175  0.4502  Correlation:                           ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_ log_k_meso_free           0.1086                            beta_pH(log_k_meso_free) -0.0426 -0.9821                    log_k_meso_free_bound     0.2513  0.1717 -0.0409            log_k_meso_bound_free     0.1297  0.1171 -0.0139  0.9224     Random effects:                      est.  lower  upper SD.log_k_meso_free 0.3338 0.2175 0.4502  Variance model:      est. lower upper a.1 5.082 4.379 5.786  Backtransformed parameters:                        est.     lower    upper meso_free_0       93.319649 9.116e+01 95.47601 k_meso_free        0.002142 5.928e-04  0.00774 k_meso_free_bound  0.022369 5.502e-03  0.09095 k_meso_bound_free  0.052539 1.417e-02  0.19478  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.09736 0.02632 0.31602   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.87 73.16    22.02         7.12        26.34 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:20 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.849 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.8 779.6 -373.9  Optimised parameters:                     est.   lower   upper meso_0          93.32599 91.4658 95.1862 log_k1          -5.81463 -7.2710 -4.3583 beta_pH(log_k1)  0.47472  0.2334  0.7160 log_k2          -6.79633 -8.7605 -4.8322 beta_pH(log_k2)  0.54151  0.2124  0.8706 log_tb           3.24674  1.2470  5.2465 beta_pH(log_tb) -0.09889 -0.4258  0.2280 a.1              4.49487  3.7766  5.2132 SD.log_k1        0.37191  0.2370  0.5068 SD.log_k2        0.29210  0.0994  0.4848 SD.log_tb        0.25353 -0.0664  0.5735  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_tb  log_k1           0.0744                                         beta_pH(log_k1) -0.0452 -0.9915                                 log_k2           0.0066 -0.0363  0.0376                         beta_pH(log_k2) -0.0071  0.0372 -0.0391 -0.9939                 log_tb          -0.0238 -0.1483  0.1362 -0.3836  0.3696         beta_pH(log_tb)  0.0097  0.1359 -0.1265  0.3736 -0.3653 -0.9905  Random effects:             est.   lower  upper SD.log_k1 0.3719  0.2370 0.5068 SD.log_k2 0.2921  0.0994 0.4848 SD.log_tb 0.2535 -0.0664 0.5735  Variance model:      est. lower upper a.1 4.495 3.777 5.213  Backtransformed parameters:             est.     lower     upper meso_0 93.325994 9.147e+01 9.519e+01 k1      0.002984 6.954e-04 1.280e-02 k2      0.001118 1.568e-04 7.969e-03 tb     25.706437 3.480e+00 1.899e+02  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 15.65 79.63    23.97   15.16   27.55 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:22 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.439 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   766.5 775.4 -373.2  Optimised parameters:                    est.    lower   upper meso_0          93.3251 91.49823 95.1520 log_k1          -5.6796 -7.08789 -4.2714 beta_pH(log_k1)  0.4567  0.22400  0.6894 log_k2          -6.6083 -8.33839 -4.8781 beta_pH(log_k2)  0.4982  0.20644  0.7899 log_tb           2.7040  2.33033  3.0777 a.1              4.4452  3.73537  5.1551 SD.log_k1        0.3570  0.22104  0.4930 SD.log_k2        0.2252  0.01864  0.4318 SD.log_tb        0.5488  0.24560  0.8521  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_k1           0.0740                                 beta_pH(log_k1) -0.0453 -0.9912                         log_k2           0.0115 -0.0650  0.0661                 beta_pH(log_k2) -0.0116  0.0649 -0.0667 -0.9936         log_tb          -0.0658 -0.1135  0.0913 -0.1500  0.1210  Random effects:             est.   lower  upper SD.log_k1 0.3570 0.22104 0.4930 SD.log_k2 0.2252 0.01864 0.4318 SD.log_tb 0.5488 0.24560 0.8521  Variance model:      est. lower upper a.1 4.445 3.735 5.155  Backtransformed parameters:             est.     lower     upper meso_0 93.325134 9.150e+01 95.152036 k1      0.003415 8.352e-04  0.013962 k2      0.001349 2.392e-04  0.007611 tb     14.939247 1.028e+01 21.707445  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 14.69 82.45    24.82   14.69   29.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Session info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  gridExtra_2.3     jquerylib_0.1.4   systemfonts_1.2.1  [9] scales_1.3.0      textshaping_1.0.0 readxl_1.4.4      yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.9.0       [25] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51         [29] fs_1.6.5          sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     [33] magrittr_2.0.3    digest_0.6.37     grid_4.5.0        mclust_6.1.1      [37] lifecycle_1.0.4   nlme_3.1-168      vctrs_0.6.5       evaluate_1.0.3    [41] glue_1.8.0        cellranger_1.1.0  codetools_0.2-20  ragg_1.3.3        [45] zoo_1.8-13        colorspace_2.1-1  tools_4.5.0       pkgconfig_2.0.3   [49] htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hardware-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Hardware info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"","what":"Test data","title":"Benchmark timings for saem.mmkin","text":"Please refer vignette dimethenamid_2018 explanation following preprocessing.","code":"dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"parent-only","dir":"Articles > Web_only","previous_headings":"Test cases","what":"Parent only","title":"Benchmark timings for saem.mmkin","text":"model comparison suggests use SFORB model two-component error. comparison, keep DFOP model two-component error, competes SFORB biphasic curves. two models, random effects transformed parameters k2 k_DMTA_bound_free quantified.","code":"parent_mods <- c(\"SFO\", \"DFOP\", \"SFORB\", \"HS\") parent_sep_const <- mmkin(parent_mods, dmta_ds, quiet = TRUE, cores = n_cores) parent_sep_tc <- update(parent_sep_const, error_model = \"tc\")  t1 <- system.time(sfo_const <- saem(parent_sep_const[\"SFO\", ]))[[\"elapsed\"]] t2 <- system.time(dfop_const <- saem(parent_sep_const[\"DFOP\", ]))[[\"elapsed\"]] t3 <- system.time(sforb_const <- saem(parent_sep_const[\"SFORB\", ]))[[\"elapsed\"]] t4 <- system.time(hs_const <- saem(parent_sep_const[\"HS\", ]))[[\"elapsed\"]] t5 <- system.time(sfo_tc <- saem(parent_sep_tc[\"SFO\", ]))[[\"elapsed\"]] t6 <- system.time(dfop_tc <- saem(parent_sep_tc[\"DFOP\", ]))[[\"elapsed\"]] t7 <- system.time(sforb_tc <- saem(parent_sep_tc[\"SFORB\", ]))[[\"elapsed\"]] t8 <- system.time(hs_tc <- saem(parent_sep_tc[\"HS\", ]))[[\"elapsed\"]] anova(   sfo_const, dfop_const, sforb_const, hs_const,   sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1) illparms(dfop_tc) ## [1] \"sd(log_k2)\" illparms(sforb_tc) ## [1] \"sd(log_k_DMTA_bound_free)\""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"one-metabolite","dir":"Articles > Web_only","previous_headings":"Test cases","what":"One metabolite","title":"Benchmark timings for saem.mmkin","text":"remove parameters found ill-defined parent fits.","code":"one_met_mods <- list(   DFOP_SFO = mkinmod(     DMTA = mkinsub(\"DFOP\", \"M23\"),     M23 = mkinsub(\"SFO\")),   SFORB_SFO = mkinmod(     DMTA = mkinsub(\"SFORB\", \"M23\"),     M23 = mkinsub(\"SFO\")))  one_met_sep_const <- mmkin(one_met_mods, dmta_ds, error_model = \"const\",   cores = n_cores, quiet = TRUE) one_met_sep_tc <- mmkin(one_met_mods, dmta_ds, error_model = \"tc\",   cores = n_cores, quiet = TRUE)  t9 <- system.time(dfop_sfo_tc <- saem(one_met_sep_tc[\"DFOP_SFO\", ],     no_random_effect = \"log_k2\"))[[\"elapsed\"]] t10 <- system.time(sforb_sfo_tc <- saem(one_met_sep_tc[\"SFORB_SFO\", ],     no_random_effect = \"log_k_DMTA_bound_free\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"three-metabolites","dir":"Articles > Web_only","previous_headings":"Test cases","what":"Three metabolites","title":"Benchmark timings for saem.mmkin","text":"case three metabolites, keep SFORB model order limit time compiling vignette, fitting parallel may disturb benchmark. , include random effects ill-defined previous fits subsets degradation model.","code":"illparms(sforb_sfo_tc) three_met_mods <- list(   SFORB_SFO3_plus = mkinmod(     DMTA = mkinsub(\"SFORB\", c(\"M23\", \"M27\", \"M31\")),     M23 = mkinsub(\"SFO\"),     M27 = mkinsub(\"SFO\"),     M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE)))  three_met_sep_tc <- mmkin(three_met_mods, dmta_ds, error_model = \"tc\",   cores = n_cores, quiet = TRUE)  t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc[\"SFORB_SFO3_plus\", ],     no_random_effect = \"log_k_DMTA_bound_free\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"results","dir":"Articles > Web_only","previous_headings":"","what":"Results","title":"Benchmark timings for saem.mmkin","text":"Benchmarks available error models shown. intended improving mkin, comparing CPUs operating systems. trademarks belong respective owners.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"parent-only-1","dir":"Articles > Web_only","previous_headings":"Results","what":"Parent only","title":"Benchmark timings for saem.mmkin","text":"Constant variance SFO, DFOP, SFORB HS. Two-component error fits SFO, DFOP, SFORB HS.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"one-metabolite-1","dir":"Articles > Web_only","previous_headings":"Results","what":"One metabolite","title":"Benchmark timings for saem.mmkin","text":"Two-component error DFOP-SFO SFORB-SFO.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"three-metabolites-1","dir":"Articles > Web_only","previous_headings":"Results","what":"Three metabolites","title":"Benchmark timings for saem.mmkin","text":"Two-component error SFORB-SFO3-plus","code":""},{"path":"https://pkgdown.jrwb.de/mkin/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Johannes Ranke. Author, maintainer, copyright holder. Katrin Lindenberger. Contributor.           contributed mkinresplot() René Lehmann. Contributor.           ilr() invilr() Eurofins Regulatory AG. Copyright holder.           copyright contributions JR 2012-2014","code":""},{"path":"https://pkgdown.jrwb.de/mkin/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ranke J (2025). mkin: Kinetic Evaluation Chemical Degradation Data. R package version 1.2.11, https://pkgdown.jrwb.de/mkin/.","code":"@Manual{,   title = {mkin: Kinetic Evaluation of Chemical Degradation Data},   author = {Johannes Ranke},   year = {2025},   note = {R package version 1.2.11},   url = {https://pkgdown.jrwb.de/mkin/}, }"},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"mkin","dir":"","previous_headings":"","what":"Kinetic Evaluation of Chemical Degradation Data","title":"Kinetic Evaluation of Chemical Degradation Data","text":"R package mkin provides calculation routines analysis chemical degradation data, including multicompartment kinetics needed modelling formation decline transformation products, several degradation compartments involved. provides stable functionality kinetic evaluations according FOCUS guidance (see details). addition, provides functionality hierarchical kinetics based nonlinear mixed-effects models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Kinetic Evaluation of Chemical Degradation Data","text":"can install latest released version CRAN within R:","code":"install.packages(\"mkin\")"},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"background","dir":"","previous_headings":"","what":"Background","title":"Kinetic Evaluation of Chemical Degradation Data","text":"regulatory evaluation chemical substances like plant protection products (pesticides), biocides chemicals, degradation data play important role. evaluation pesticide degradation experiments, detailed guidance various helpful tools developed detailed ‘Credits historical remarks’ . package aims provide one stop solution degradation kinetics, addressing modellers willing , even prefer work R.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"basic-usage","dir":"","previous_headings":"","what":"Basic usage","title":"Kinetic Evaluation of Chemical Degradation Data","text":"start, look code examples provided plot.mkinfit plot.mmkin, package vignettes FOCUS L FOCUS D.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Kinetic Evaluation of Chemical Degradation Data","text":"HTML documentation latest version released CRAN available jrwb.de github. Documentation development version found ‘dev’ subdirectory. articles section documentation, can also find demonstrations application nonlinear hierarchical models, also known nonlinear mixed-effects models, complex data, including transformation products covariates.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"general","dir":"","previous_headings":"Features","what":"General","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Highly flexible model specification using mkinmod, including equilibrium reactions using single first-order reversible binding (SFORB) model, automatically create two state variables observed variable. Model solution (forward modelling) function mkinpredict performed either using analytical solution case parent degradation simple models involving single transformation product, , eigenvalue based solution simple first-order (SFO) SFORB kinetics used model, using numeric solver deSolve package (default lsoda). usual one-sided t-test significant difference zero shown based estimators untransformed parameters. Summary plotting functions. summary mkinfit object fact full report give enough information able approximately reproduce fit tools. chi-squared error level defined FOCUS kinetics guidance (see ) calculated observed variable. ‘variance variable’ error model often fitted using Iteratively Reweighted Least Squares (IRLS) can specified error_model = \"obs\".","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"unique-in-mkin","dir":"","previous_headings":"Features","what":"Unique in mkin","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Three different error models can selected using argument error_model mkinfit function. two-component error model similar one proposed Rocke Lorenzato can selected using argument error_model = \"tc\". Model comparisons using Akaike Information Criterion (AIC) supported can also used non-constant variance. cases FOCUS chi-squared error level meaningful. default, kinetic rate constants kinetic formation fractions transformed internally using transform_odeparms estimators can reasonably expected follow normal distribution. parameter estimates backtransformed match model definition, confidence intervals calculated standard errors also backtransformed correct scale, include meaningless values like negative rate constants formation fractions adding 1, occur single experiment single defined radiolabel position. metabolite decline phase described well SFO kinetics, SFORB kinetics can used metabolite. Mathematically, SFORB model equivalent DFOP model. However, SFORB model advantage mechanistic interpretation model parameters. Nonlinear mixed-effects models (hierarchical models) can created fits degradation model different datasets compound using nlme.mmkin saem.mmkin methods. Note convergence nlme fits depends quality data. Convergence better simple models data many groups (e.g. soils). saem method uses saemix package backend. Analytical solutions suitable use package implemented parent models important models including one metabolite (SFO-SFO DFOP-SFO). Fitting models saem.mmkin, makes use compiled ODE models mkin provides, longer run times (couple minutes hour).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"performance","dir":"","previous_headings":"Features","what":"Performance","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Parallel fitting several models several datasets supported, see example plot.mmkin. C compiler installed, kinetic models compiled automatically generated C code, see vignette compiled_models. autogeneration C code inspired ccSolve package. Thanks Karline Soetaert work . Even compiler installed, many degradation models still give good performance, current versions mkin also analytical solutions models one metabolite, SFO SFORB used parent compound, Eigenvalue based solutions degradation model available.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"gui","dir":"","previous_headings":"","what":"GUI","title":"Kinetic Evaluation of Chemical Degradation Data","text":"graphical user interface may useful. Please refer documentation page installation instructions manual. supports evaluations using (generalised) nonlinear regression, simultaneous fits using nonlinear mixed-effects models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"news","dir":"","previous_headings":"","what":"News","title":"Kinetic Evaluation of Chemical Degradation Data","text":"list changes latest CRAN release one github branch, e.g. main branch.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"credits-and-historical-remarks","dir":"","previous_headings":"","what":"Credits and historical remarks","title":"Kinetic Evaluation of Chemical Degradation Data","text":"mkin possible without underlying software stack consisting , among others, R package deSolve. previous version, mkin also using functionality FME package. Please refer package page CRAN full list imported suggested R packages. Also, Debian Linux, vim editor Nvim-R plugin invaluable development. mkin written without introduced regulatory fate modelling pesticides Adrian Gurney time Harlan Laboratories Ltd (formerly RCC Ltd). mkin greatly profits largely follows work done FOCUS Degradation Kinetics Workgroup, detailed guidance document 2006, slightly updated 2011 2014. Also, inspired first version KinGUI developed BayerCropScience, based MatLab runtime environment. companion package kinfit (now deprecated) started 2008 first published CRAN 01 May 2010. first mkin code published 11 May 2010 first CRAN version 18 May 2010. 2011, Bayer Crop Science started distribute R based successor KinGUI named KinGUII whose R code based mkin, added, among refinements, closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation variance observed variables, Markov Chain Monte Carlo (MCMC) simulation functionality, similar available e.g. FME package. Somewhat parallel, Syngenta sponsored development mkin KinGUII based GUI application called CAKE, also adds IRLS MCMC, limited model formulation, puts weight usability. CAKE available download CAKE website, can also find zip archive R scripts derived mkin, published GPL license. Finally, KineticEval, contains development scripts used KinGUII. Thanks René Lehmann, formerly working Umweltbundesamt, nice cooperation parameter transformations, especially isometric log-ratio transformation now used formation fractions case two transformation targets. Many inspirations improvements mkin resulted kinetic evaluations degradation data clients working Harlan Laboratories Eurofins Regulatory AG, now independent consultant. Funding received Umweltbundesamt course projects Project Number 27452 (Testing validation modelling software alternative ModelMaker 4.0, 2014-2015) Project Number 56703 (Optimization gmkin routine use Umweltbundesamt, 2015) Project Number 92570 (Update Project Number 27452, 2017-2018) Project Number 112407 (Testing feasibility using error model according Rocke Lorenzato realistic parameter estimates kinetic evaluation degradation data, 2018-2019) Project Number 120667 (Development objective criteria evaluation visual fit kinetic evaluation degradation data, 2019-2020) Project Number 146839 (Checking feasibility using mixed-effects models derivation kinetic modelling parameters degradation studies, 2020-2021) Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) Thanks everyone involved collaboration support! Thanks due also Emmanuelle Comets, maintainer saemix package, interest support using SAEM algorithm implementation saemix evaluation chemical degradation data. Regarding application nonlinear mixed-effects models degradation data, von Götz et al (1999) already proposed use technique context environmental risk assessments pesticides. However, work apparently followed , independently arrive idea missed cite previous work topic first publications.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"development","dir":"","previous_headings":"","what":"Development","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Contributions welcome!","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"Provides convenient way compare different kinetic models fitted dataset.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"","code":"# S3 method for class 'mmkin' AIC(object, ..., k = 2)  # S3 method for class 'mmkin' BIC(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"object object class mmkin, containing one column. ... compatibility generic method k generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"generic method (numeric value single fits, dataframe several fits column).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"","code":"# skip, as it takes > 10 s on winbuilder   f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"),     list(\"FOCUS A\" = FOCUS_2006_A,          \"FOCUS C\" = FOCUS_2006_C), cores = 1, quiet = TRUE) #> Warning: Optimisation did not converge: #> false convergence (8)   # We get a warning because the FOMC model does not converge for the   # FOCUS A dataset, as it is well described by SFO    AIC(f[\"SFO\", \"FOCUS A\"]) # We get a single number for a single fit #> [1] 55.28197   AIC(f[[\"SFO\", \"FOCUS A\"]]) # or when extracting an mkinfit object #> [1] 55.28197    # For FOCUS A, the models fit almost equally well, so the higher the number   # of parameters, the higher (worse) the AIC   AIC(f[, \"FOCUS A\"]) #>      df      AIC #> SFO   3 55.28197 #> FOMC  4 57.28198 #> DFOP  5 59.28197   AIC(f[, \"FOCUS A\"], k = 0) # If we do not penalize additional parameters, we get nearly the same #>      df      AIC #> SFO   3 49.28197 #> FOMC  4 49.28198 #> DFOP  5 49.28197   BIC(f[, \"FOCUS A\"])        # Comparing the BIC gives a very similar picture #>      df      BIC #> SFO   3 55.52030 #> FOMC  4 57.59974 #> DFOP  5 59.67918    # For FOCUS C, the more complex models fit better   AIC(f[, \"FOCUS C\"]) #>      df      AIC #> SFO   3 59.29336 #> FOMC  4 44.68652 #> DFOP  5 29.02372   BIC(f[, \"FOCUS C\"]) #>      df      BIC #> SFO   3 59.88504 #> FOMC  4 45.47542 #> DFOP  5 30.00984"},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Export a list of datasets format to a CAKE study file — CAKE_export","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"addition datasets, pathways degradation model can specified well.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"","code":"CAKE_export(   ds,   map = c(parent = \"Parent\"),   links = NA,   filename = \"CAKE_export.csf\",   path = \".\",   overwrite = FALSE,   study = \"Degradinol aerobic soil degradation\",   description = \"\",   time_unit = \"days\",   res_unit = \"% AR\",   comment = \"\",   date = Sys.Date(),   optimiser = \"IRLS\" )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"ds named list datasets long format compatible mkinfit. map character vector CAKE compartment names (Parent, A1, ...), named names used list datasets. links optional character vector target compartments, named names source compartments. order make easier, names used datasets supplied. filename write result. end .csf order compatible CAKE. path optional path output file. overwrite TRUE, existing files overwritten. study name study. description optional description. time_unit time unit residue data. res_unit unit used residues. comment optional comment. date date file creation. optimiser Can OLS IRLS.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"five datasets extracted active substance evaluation dossier published EFSA. Kinetic evaluations shown datasets intended illustrate advance kinetic modelling. fact data results shown imply license use context pesticide  registrations, use data may constrained data protection regulations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"","code":"D24_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"mkindsg object grouping five datasets","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"Hellenic Ministry Rural Development Agriculture (2014) Final addendum Renewal Assessment Report - public version - 2,4-D Volume 3 Annex B.8 Fate behaviour environment https://open.efsa.europa.eu/study-inventory/EFSA-Q-2013-00811","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"Data first dataset p. 685. Data four datasets used preprocessed versions given kinetics section (p. 761ff.), exception residues smaller 1 DCP soil Site I2, values given p. 694 used. R code used create data object installed package 'dataset_generation' directory. code, page numbers given specific pieces information comments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"","code":"print(D24_2014) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Aerobic soil degradation data on 2,4-D from the EU assessment in 2014  #> Occurrence of observed compounds $observed_n: #> D24 DCP DCA  #>   5   4   4  #> Time normalisation factors $f_time_norm: #> [1] 1.6062378 0.7118732 0.7156063 0.7156063 0.8977124 #> Meta information $meta: #>                                 study usda_soil_type study_moisture_ref_type #> Mississippi                Cohen 1991      Silt loam                    <NA> #> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1 #> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1 #> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1 #> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1 #>             rel_moisture temperature #> Mississippi           NA          25 #> Fayette              0.5          20 #> RefSol 03-G          0.5          20 #> Site E1              0.5          20 #> Site I2              0.5          20 # \\dontrun{ print(D24_2014$ds[[1]], data = TRUE) #> <mkinds> with $title:  Mississippi  #> Observed compounds $observed:  D24  #> Sampling times $sampling_times: #> 0, 2, 4, 7, 15, 24, 35, 56, 71, 114, 183, 273, 365  #> With a maximum of  1  replicates #>    time  D24 #> 1     0 96.8 #> 2     2 81.0 #> 3     4 81.7 #> 4     7 88.2 #> 5    15 66.3 #> 6    24 72.9 #> 7    35 62.6 #> 8    56 54.6 #> 9    71 35.2 #> 10  114 18.0 #> 11  183 11.3 #> 12  273  9.9 #> 13  365  6.3 m_D24 = mkinmod(D24 = mkinsub(\"SFO\", to = \"DCP\"),   DCP = mkinsub(\"SFO\", to = \"DCA\"),   DCA = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(m_D24) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $D24 #> $type: SFO; $to: DCP; $sink: TRUE #> $DCP #> $type: SFO; $to: DCA; $sink: TRUE #> $DCA #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_D24/dt = - k_D24 * D24 #> d_DCP/dt = + f_D24_to_DCP * k_D24 * D24 - k_DCP * DCP #> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA m_D24_2 = mkinmod(D24 = mkinsub(\"DFOP\", to = \"DCP\"),   DCP = mkinsub(\"SFO\", to = \"DCA\"),   DCA = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(m_D24_2) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $D24 #> $type: DFOP; $to: DCP; $sink: TRUE #> $DCP #> $type: SFO; $to: DCA; $sink: TRUE #> $DCA #> $type: SFO; $sink: TRUE #> Compiled model $cf available #> Differential equations: #> d_D24/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * D24 #> d_DCP/dt = + f_D24_to_DCP * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * #>            exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 #>            * time))) * D24 - k_DCP * DCP #> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Double First-Order in Parallel kinetics — DFOP.solution","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"Function describing decline defined starting value using sum two exponential decline functions.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"","code":"DFOP.solution(t, parent_0, k1, k2, g)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"t Time. parent_0 Starting value response variable time zero. k1 First kinetic constant. k2 Second kinetic constant. g Fraction starting value declining according first kinetic constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"","code":"plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Subsetting method for mmkin objects — [.mmkin","title":"Subsetting method for mmkin objects — [.mmkin","text":"Subsetting method mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subsetting method for mmkin objects — [.mmkin","text":"","code":"# S3 method for class 'mmkin' x[i, j, ..., drop = FALSE]"},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subsetting method for mmkin objects — [.mmkin","text":"x mmkin object Row index selecting fits specific models j Column index selecting fits specific datasets ... used, satisfy generic method definition drop FALSE, method always returns mmkin object, otherwise either list mkinfit objects single mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subsetting method for mmkin objects — [.mmkin","text":"object class mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Subsetting method for mmkin objects — [.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subsetting method for mmkin objects — [.mmkin","text":"","code":"# Only use one core, to pass R CMD check --as-cran   fits <- mmkin(c(\"SFO\", \"FOMC\"), list(B = FOCUS_2006_B, C = FOCUS_2006_C),                 cores = 1, quiet = TRUE)   fits[\"FOMC\", ] #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  B  C  #>   FOMC OK OK #>  #> OK: No warnings   fits[, \"B\"] #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  B  #>   SFO  OK #>   FOMC OK #>  #> OK: No warnings   fits[\"SFO\", \"B\"] #> <mmkin> object #> Status of individual fits: #>  #>      dataset #> model B  #>   SFO OK #>  #> OK: No warnings    head(     # This extracts an mkinfit object with lots of components     fits[[\"FOMC\", \"B\"]]   ) #> $par #>  parent_0 log_alpha  log_beta     sigma  #> 99.666192  2.549850  5.050587  1.890202  #>  #> $objective #> [1] 28.58291 #>  #> $convergence #> [1] 0 #>  #> $iterations #> [1] 21 #>  #> $evaluations #> function gradient  #>       25       78  #>  #> $message #> [1] \"both X-convergence and relative convergence (5)\" #>"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"","code":"FOCUS_2006_DFOP_ref_A_to_B"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound f fitted f parameter k1 fitted k1 parameter k2 fitted k2 parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"","code":"data(FOCUS_2006_DFOP_ref_A_to_B)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"","code":"FOCUS_2006_FOMC_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound alpha fitted alpha parameter beta fitted beta parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"","code":"data(FOCUS_2006_FOMC_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"","code":"FOCUS_2006_HS_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound tb fitted tb parameter k1 fitted k1 parameter k2 fitted k2 parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"","code":"data(FOCUS_2006_HS_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"","code":"FOCUS_2006_SFO_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound k fitted first-order degradation rate constant DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"","code":"data(FOCUS_2006_SFO_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"Data taken FOCUS (2006), p. 258.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"","code":"FOCUS_2006_A   FOCUS_2006_B   FOCUS_2006_C   FOCUS_2006_D   FOCUS_2006_E   FOCUS_2006_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"6 datasets observations following variables. name factor containing name observed variable time numeric vector containing time points value numeric vector containing concentrations percent applied radioactivity","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"","code":"FOCUS_2006_C #>     name time value #> 1 parent    0  85.1 #> 2 parent    1  57.9 #> 3 parent    3  29.9 #> 4 parent    7  14.6 #> 5 parent   14   9.7 #> 6 parent   28   6.6 #> 7 parent   63   4.0 #> 8 parent   91   3.9 #> 9 parent  119   0.6"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"First-Order Multi-Compartment kinetics — FOMC.solution","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"Function describing exponential decline defined starting value, decreasing rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"","code":"FOMC.solution(t, parent_0, alpha, beta)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"t Time. parent_0 Starting value response variable time zero. alpha Shape parameter determined coefficient variation rate constant values. beta Location parameter.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"form given differs slightly original reference Gustafson Holden (1990). parameter beta corresponds 1/beta original equation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"solution FOMC kinetic model reduces SFO.solution large values alpha beta \\(k = \\frac{\\beta}{\\alpha}\\).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics Gustafson DI Holden LR (1990) Nonlinear pesticide dissipation soil: new model based spatial variability. Environmental Science Technology 24, 1032-1038","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"","code":"plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Hockey-Stick kinetics — HS.solution","title":"Hockey-Stick kinetics — HS.solution","text":"Function describing two exponential decline functions break point .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hockey-Stick kinetics — HS.solution","text":"","code":"HS.solution(t, parent_0, k1, k2, tb)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hockey-Stick kinetics — HS.solution","text":"t Time. parent_0 Starting value response variable time zero. k1 First kinetic constant. k2 Second kinetic constant. tb Break point. time, exponential decline according k1 calculated, time, exponential decline proceeds according k2.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hockey-Stick kinetics — HS.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hockey-Stick kinetics — HS.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hockey-Stick kinetics — HS.solution","text":"","code":"plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Indeterminate order rate equation kinetics — IORE.solution","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"Function describing exponential decline defined starting value, concentration dependent rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"","code":"IORE.solution(t, parent_0, k__iore, N)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"t Time. parent_0 Starting value response variable time zero. k__iore Rate constant. Note depends concentration units used. N Exponent describing nonlinearity rate equation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"solution IORE kinetic model reduces SFO.solution N = 1.  parameters IORE model can transformed equivalent parameters FOMC mode - see NAFTA guidance details.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"NAFTA Technical Working Group Pesticides (dated) Guidance Evaluating Calculating Degradation Kinetics Environmental Media","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"","code":"plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))    # \\dontrun{     fit.fomc <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)     fit.iore <- mkinfit(\"IORE\", FOCUS_2006_C, quiet = TRUE)     fit.iore.deS <- mkinfit(\"IORE\", FOCUS_2006_C, solution_type = \"deSolve\", quiet = TRUE) #> Error in is.loaded(initfunc, PACKAGE = dllname, type = \"\") :  #>   invalid 'PACKAGE' argument      print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par,                      row.names = paste(\"model par\", 1:4))) #>             fit.fomc.par fit.iore.par fit.iore.deS.par #> model par 1  85.87489063    85.874890        85.874890 #> model par 2   0.05192238    -4.826631        -4.826631 #> model par 3   0.65096665     1.949403         1.949403 #> model par 4   1.85744396     1.857444         1.857444     print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,                 iore.deS = endpoints(fit.iore)$distimes)) #>              DT50    DT90 DT50back #> fomc     1.785233 15.1479 4.559973 #> iore     1.785233 15.1479 4.559973 #> iore.deS 1.785233 15.1479 4.559973   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":null,"dir":"Reference","previous_headings":"","what":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"Data taken US EPA (2015), p. 19 23.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"","code":"NAFTA_SOP_Appendix_B   NAFTA_SOP_Appendix_D"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"2 datasets observations following variables. name factor containing name observed variable time numeric vector containing time points value numeric vector containing concentrations","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics   environmental media. NAFTA Technical Working Group Pesticides   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation   accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance   Calculate Representative Half-life Values Characterizing Pesticide   Degradation   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"","code":"nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> 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   print(nafta_evaluation) #> Sums of squares: #>       SFO      IORE      DFOP  #> 1378.6832  615.7730  517.8836  #>  #> Critical sum of squares for checking the SFO model: #> [1] 717.4598 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)    Lower   Upper #> parent_0  83.7558 1.80e-14 77.18268 90.3288 #> k_parent   0.0017 7.43e-05  0.00112  0.0026 #> sigma      8.7518 1.22e-05  5.64278 11.8608 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       9.69e+01     NA 8.88e+01 1.05e+02 #> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09 #> N_parent       6.68e+00     NA 4.19e+00 9.17e+00 #> sigma          5.85e+00     NA 3.76e+00 7.94e+00 #>  #> $DFOP #>          Estimate   Pr(>t)    Lower    Upper #> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 #> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02 #> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03 #> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01 #> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00 #>  #>  #> DTx values: #>      DT50    DT90 DT50_rep #> SFO   407    1350      407 #> IORE  541 5190000  1560000 #> DFOP  429    2380      841 #>  #> Representative half-life: #> [1] 841.41   plot(nafta_evaluation)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":null,"dir":"Reference","previous_headings":"","what":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"Data taken Attachment 1 SOP.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"","code":"NAFTA_SOP_Attachment"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"list (NAFTA_SOP_Attachment) containing 16 datasets suitable   evaluation nafta","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics   environmental media. NAFTA Technical Working Group Pesticides   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation   accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance   Calculate Representative Half-life Values Characterizing Pesticide   Degradation   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"","code":"nafta_att_p5a <- nafta(NAFTA_SOP_Attachment[[\"p5a\"]], cores = 1) #> 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   print(nafta_att_p5a) #> Sums of squares: #>       SFO      IORE      DFOP  #> 465.21753  56.27506  32.06401  #>  #> Critical sum of squares for checking the SFO model: #> [1] 64.4304 #>  #> Parameters: #> $SFO #>          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 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 #> g        6.47e-01 3.67e-06  0.6248   0.6677 #> sigma    1.27e+00 8.91e-06  0.8395   1.6929 #>  #>  #> DTx values: #>      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 #>  #> Representative half-life: #> [1] 321.51   plot(nafta_att_p5a)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Single First-Order kinetics — SFO.solution","title":"Single First-Order kinetics — SFO.solution","text":"Function describing exponential decline defined starting value.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single First-Order kinetics — SFO.solution","text":"","code":"SFO.solution(t, parent_0, k)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single First-Order kinetics — SFO.solution","text":"t Time. parent_0 Starting value response variable time zero. k Kinetic rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single First-Order kinetics — SFO.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single First-Order kinetics — SFO.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single First-Order kinetics — SFO.solution","text":"","code":"plot(function(x) SFO.solution(x, 100, 3), 0, 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Single First-Order Reversible Binding kinetics — SFORB.solution","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"Function describing solution differential equations describing kinetic model first-order terms two-way transfer free bound fraction, first-order degradation term free fraction.  initial condition defined amount free fraction substance bound fraction.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"","code":"SFORB.solution(t, parent_0, k_12, k_21, k_1output)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"t Time. parent_0 Starting value response variable time zero. k_12 Kinetic constant describing transfer free bound. k_21 Kinetic constant describing transfer bound free. k_1output Kinetic constant describing degradation free fraction.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"value response variable, sum free bound fractions time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"","code":"plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":null,"dir":"Reference","previous_headings":"","what":"Add normally distributed errors to simulated kinetic degradation data — add_err","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Normally distributed errors added data predicted specific degradation model using mkinpredict. variance error may depend predicted value specified standard deviation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"","code":"add_err(   prediction,   sdfunc,   secondary = c(\"M1\", \"M2\"),   n = 10,   LOD = 0.1,   reps = 2,   digits = 1,   seed = NA )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"prediction prediction kinetic model produced mkinpredict. sdfunc function taking predicted value argument returning standard deviation used generating random error terms value. secondary names state variables initial value zero n number datasets generated. LOD limit detection (LOD). Values LOD adding random error set NA. reps number replicates generated within datasets. digits number digits values rounded. seed seed used generation random numbers. NA, seed set.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"list datasets compatible mmkin, .e. components list datasets compatible mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Ranke J Lehmann R (2015) t-test t-test, question. XV Symposium Pesticide Chemistry 2-4 September 2015, Piacenza, Italy https://jrwb.de/posters/piacenza_2015.pdf","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"","code":"# The kinetic model m_SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                      M1 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  # Generate a prediction for a specific set of parameters sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)  # This is the prediction used for the \"Type 2 datasets\" on the Piacenza poster # from 2015 d_SFO_SFO <- mkinpredict(m_SFO_SFO,                          c(k_parent = 0.1, f_parent_to_M1 = 0.5,                            k_M1 = log(2)/1000),                          c(parent = 100, M1 = 0),                          sampling_times)  # Add an error term with a constant (independent of the value) standard deviation # of 10, and generate three datasets d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )  # Name the datasets for nicer plotting names(d_SFO_SFO_err) <- paste(\"Dataset\", 1:3)  # Name the model in the list of models (with only one member in this case) for # nicer plotting later on.  Be quiet and use only one core not to offend CRAN # checks # \\dontrun{ f_SFO_SFO <- mmkin(list(\"SFO-SFO\" = m_SFO_SFO),                    d_SFO_SFO_err, cores = 1,                    quiet = TRUE)  plot(f_SFO_SFO)   # We would like to inspect the fit for dataset 3 more closely # Using double brackets makes the returned object an mkinfit object # instead of a list of mkinfit objects, so plot.mkinfit is used plot(f_SFO_SFO[[3]], show_residuals = TRUE)   # If we use single brackets, we should give two indices (model and dataset), # and plot.mmkin is used plot(f_SFO_SFO[1, 3])  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Anova method for saem.mmkin objects — anova.saem.mmkin","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"Generate anova object. method calculate BIC saemix package. prominent anova methods, models sorted number parameters, tests (requested) always relative model previous line.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' anova(   object,   ...,   method = c(\"is\", \"lin\", \"gq\"),   test = FALSE,   model.names = NULL )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"object saem.mmkin object ... objects method Method likelihood calculation: \"\" (importance sampling), \"lin\" (linear approximation), \"gq\" (Gaussian quadrature). Passed saemix::logLik.SaemixObject test likelihood ratio test performed? TRUE, alternative models tested first model. done nested models. model.names Optional character vector model names","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"\"anova\" data frame; traditional (S3) result anova()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike weights for model averaging — aw","title":"Calculate Akaike weights for model averaging — aw","text":"Akaike weights calculated based relative expected Kullback-Leibler information specified Burnham Anderson (2004).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike weights for model averaging — aw","text":"","code":"aw(object, ...)  # S3 method for class 'mkinfit' aw(object, ...)  # S3 method for class 'mmkin' aw(object, ...)  # S3 method for class 'mixed.mmkin' aw(object, ...)  # S3 method for class 'multistart' aw(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike weights for model averaging — aw","text":"object mmkin column object, containing two mkinfit models fitted data, mkinfit object. latter case, mkinfit objects fitted data specified dots arguments. ... used method mmkin column objects, mkinfit objects method mkinfit objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Akaike weights for model averaging — aw","text":"Burnham KP Anderson DR (2004) Multimodel Inference: Understanding AIC BIC Model Selection. Sociological Methods & Research 33(2) 261-304","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike weights for model averaging — aw","text":"","code":"# \\dontrun{ f_sfo <- mkinfit(\"SFO\", FOCUS_2006_D, quiet = TRUE) f_dfop <- mkinfit(\"DFOP\", FOCUS_2006_D, quiet = TRUE) aw_sfo_dfop <- aw(f_sfo, f_dfop) sum(aw_sfo_dfop) #> [1] 1 aw_sfo_dfop # SFO gets more weight as it has less parameters and a similar fit #> [1] 0.5970258 0.4029742 f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), list(\"FOCUS D\" = FOCUS_2006_D), cores = 1, quiet = TRUE) aw(f) #> [1] 0.4808722 0.1945539 0.3245740 sum(aw(f)) #> [1] 1 aw(f[c(\"SFO\", \"DFOP\")]) #> [1] 0.5970258 0.4029742 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if fit within an mhmkin object failed — check_failed","title":"Check if fit within an mhmkin object failed — check_failed","text":"Check fit within mhmkin object failed","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if fit within an mhmkin object failed — check_failed","text":"","code":"check_failed(x)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if fit within an mhmkin object failed — check_failed","text":"x object checked","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"default method 'quadratic' based quadratic approximation curvature likelihood function maximum likelihood parameter estimates. alternative method 'profile' based profile likelihood parameter. 'profile' method uses two nested optimisations can take long time, even parallelized specifying 'cores' unixoid platforms. speed method likely improved using method Venzon Moolgavkar (1988).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"","code":"# S3 method for class 'mkinfit' confint(   object,   parm,   level = 0.95,   alpha = 1 - level,   cutoff,   method = c(\"quadratic\", \"profile\"),   transformed = TRUE,   backtransform = TRUE,   cores = parallel::detectCores(),   rel_tol = 0.01,   quiet = FALSE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"object mkinfit object parm vector names parameters given confidence intervals. missing, parameters considered. level confidence level required alpha allowed error probability, overrides 'level' specified. cutoff Possibility specify alternative cutoff difference log-likelihoods confidence boundary. Specifying explicit cutoff value overrides arguments 'level' 'alpha' method 'quadratic' method approximates likelihood function optimised parameters using second term Taylor expansion, using second derivative (hessian) contained object. 'profile' method searches parameter space cutoff confidence intervals means likelihood ratio test. transformed quadratic approximation used, applied likelihood based transformed parameters? backtransform approximate likelihood terms transformed parameters, backtransform parameters confidence intervals? cores number cores used multicore processing. Windows machines, cores > 1 currently supported. rel_tol method 'profile', accuracy lower upper bounds, relative estimate obtained quadratic method? quiet suppress message \"Profiling likelihood\" ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"matrix columns giving lower upper confidence limits parameter.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"Bates DM Watts GW (1988) Nonlinear regression analysis & applications Pawitan Y (2013) likelihood - Statistical modelling inference using likelihood. Clarendon Press, Oxford. Venzon DJ Moolgavkar SH (1988) Method Computing Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, 87–94.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"","code":"f <- mkinfit(\"SFO\", FOCUS_2006_C, quiet = TRUE) confint(f, method = \"quadratic\") #>                2.5%      97.5% #> parent_0 71.8242430 93.1600766 #> k_parent  0.2109541  0.4440528 #> sigma     1.9778868  7.3681380  # \\dontrun{ confint(f, method = \"profile\") #> Profiling the likelihood #>                2.5%      97.5% #> parent_0 73.0641834 92.1392181 #> k_parent  0.2170293  0.4235348 #> sigma     3.1307772  8.0628314  # Set the number of cores for the profiling method for further examples if (identical(Sys.getenv(\"NOT_CRAN\"), \"true\")) {   n_cores <- parallel::detectCores() - 1 } else {   n_cores <- 1 } if (Sys.getenv(\"TRAVIS\") != \"\") n_cores = 1 if (Sys.info()[\"sysname\"] == \"Windows\") n_cores = 1  SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\"),   use_of_ff = \"min\", quiet = TRUE) SFO_SFO.ff <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\"),   use_of_ff = \"max\", quiet = TRUE) f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE) system.time(ci_profile <- confint(f_d_1, method = \"profile\", cores = 1, quiet = TRUE)) #>    user  system elapsed  #>   1.194   0.000   1.194  # Using more cores does not save much time here, as parent_0 takes up most of the time # If we additionally exclude parent_0 (the confidence of which is often of # minor interest), we get a nice performance improvement if we use at least 4 cores system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = \"profile\",   c(\"k_parent_sink\", \"k_parent_m1\", \"k_m1_sink\", \"sigma\"), cores = n_cores)) #> Profiling the likelihood #>    user  system elapsed  #>   0.439   0.119   0.304  ci_profile #>                       2.5%        97.5% #> parent_0      96.456003640 1.027703e+02 #> k_parent_sink  0.040762501 5.549764e-02 #> k_parent_m1    0.046786482 5.500879e-02 #> k_m1_sink      0.003892605 6.702778e-03 #> sigma          2.535612399 3.985263e+00 ci_quadratic_transformed <- confint(f_d_1, method = \"quadratic\") ci_quadratic_transformed #>                       2.5%        97.5% #> parent_0      96.403841640 1.027931e+02 #> k_parent_sink  0.041033378 5.596269e-02 #> k_parent_m1    0.046777902 5.511931e-02 #> k_m1_sink      0.004012217 6.897547e-03 #> sigma          2.396089689 3.854918e+00 ci_quadratic_untransformed <- confint(f_d_1, method = \"quadratic\", transformed = FALSE) ci_quadratic_untransformed #>                       2.5%        97.5% #> parent_0      96.403841645 102.79312449 #> k_parent_sink  0.040485331   0.05535491 #> k_parent_m1    0.046611582   0.05494364 #> k_m1_sink      0.003835483   0.00668582 #> sigma          2.396089689   3.85491806 # Against the expectation based on Bates and Watts (1988), the confidence # intervals based on the internal parameter transformation are less # congruent with the likelihood based intervals. Note the superiority of the # interval based on the untransformed fit for k_m1_sink rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile) rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile) rel_diffs_transformed < rel_diffs_untransformed #>                2.5% 97.5% #> parent_0      FALSE FALSE #> k_parent_sink  TRUE FALSE #> k_parent_m1    TRUE FALSE #> k_m1_sink     FALSE FALSE #> sigma         FALSE FALSE signif(rel_diffs_transformed, 3) #>                   2.5%    97.5% #> parent_0      0.000541 0.000222 #> k_parent_sink 0.006650 0.008380 #> k_parent_m1   0.000183 0.002010 #> k_m1_sink     0.030700 0.029100 #> sigma         0.055000 0.032700 signif(rel_diffs_untransformed, 3) #>                   2.5%    97.5% #> parent_0      0.000541 0.000222 #> k_parent_sink 0.006800 0.002570 #> k_parent_m1   0.003740 0.001180 #> k_m1_sink     0.014700 0.002530 #> sigma         0.055000 0.032700   # Investigate a case with formation fractions f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE) ci_profile_ff <- confint(f_d_2, method = \"profile\", cores = n_cores) #> Profiling the likelihood ci_profile_ff #>                        2.5%        97.5% #> parent_0       96.456003640 1.027703e+02 #> k_parent        0.090911032 1.071578e-01 #> k_m1            0.003892606 6.702775e-03 #> f_parent_to_m1  0.471328495 5.611550e-01 #> sigma           2.535612399 3.985263e+00 ci_quadratic_transformed_ff <- confint(f_d_2, method = \"quadratic\") ci_quadratic_transformed_ff #>                        2.5%        97.5% #> parent_0       96.403833581 102.79311649 #> k_parent        0.090823771   0.10725430 #> k_m1            0.004012219   0.00689755 #> f_parent_to_m1  0.469118824   0.55959615 #> sigma           2.396089689   3.85491806 ci_quadratic_untransformed_ff <- confint(f_d_2, method = \"quadratic\", transformed = FALSE) ci_quadratic_untransformed_ff #>                        2.5%        97.5% #> parent_0       96.403833586 1.027931e+02 #> k_parent        0.090491913 1.069035e-01 #> k_m1            0.003835485 6.685823e-03 #> f_parent_to_m1  0.469113477 5.598387e-01 #> sigma           2.396089689 3.854918e+00 rel_diffs_transformed_ff <- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff) rel_diffs_untransformed_ff <- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff) # While the confidence interval for the parent rate constant is closer to # the profile based interval when using the internal parameter # transformation, the interval for the metabolite rate constant is 'better # without internal parameter transformation. rel_diffs_transformed_ff < rel_diffs_untransformed_ff #>                 2.5% 97.5% #> parent_0       FALSE FALSE #> k_parent        TRUE  TRUE #> k_m1           FALSE FALSE #> f_parent_to_m1  TRUE FALSE #> sigma           TRUE FALSE rel_diffs_transformed_ff #>                        2.5%        97.5% #> parent_0       0.0005408690 0.0002217233 #> k_parent       0.0009598532 0.0009001864 #> k_m1           0.0307283045 0.0290588367 #> f_parent_to_m1 0.0046881768 0.0027780062 #> sigma          0.0550252516 0.0327066836 rel_diffs_untransformed_ff #>                        2.5%        97.5% #> parent_0       0.0005408689 0.0002217233 #> k_parent       0.0046102155 0.0023732280 #> k_m1           0.0146740687 0.0025291815 #> f_parent_to_m1 0.0046995210 0.0023457712 #> sigma          0.0550252516 0.0327066836  # The profiling for the following fit does not finish in a reasonable time, # therefore we use the quadratic approximation m_synth_DFOP_par <- mkinmod(parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),   M1 = mkinsub(\"SFO\"),   M2 = mkinsub(\"SFO\"),   use_of_ff = \"max\", quiet = TRUE) DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = \"tc\",   error_model_algorithm = \"direct\", quiet = TRUE) confint(f_tc_2, method = \"quadratic\") #>                        2.5%        97.5% #> parent_0       94.596181875 106.19936592 #> k_M1            0.037605432   0.04490757 #> k_M2            0.008568745   0.01087675 #> f_parent_to_M1  0.021464676   0.62023880 #> f_parent_to_M2  0.015167158   0.37975350 #> k1              0.273897535   0.33388072 #> k2              0.018614555   0.02250379 #> g               0.671943738   0.73583261 #> sigma_low       0.251283679   0.83992102 #> rsd_high        0.040411022   0.07662008 confint(f_tc_2, \"parent_0\", method = \"quadratic\") #>              2.5%    97.5% #> parent_0 94.59618 106.1994 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":null,"dir":"Reference","previous_headings":"","what":"Create degradation functions for known analytical solutions — create_deg_func","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"Create degradation functions known analytical solutions","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"","code":"create_deg_func(spec, use_of_ff = c(\"min\", \"max\"))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"spec List model specifications contained mkinmod objects use_of_ff Minimum maximum use formation fractions","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"Degradation function attached mkinmod objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"","code":"SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) # to avoid warnings fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE) # \\dontrun{ fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE) if (require(rbenchmark))   benchmark(     analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE),     deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE),     replications = 2) #> Loading required package: rbenchmark #>         test replications elapsed relative user.self sys.self user.child #> 1 analytical            2   0.239    1.000     0.239        0          0 #> 2    deSolve            2   0.297    1.243     0.297        0          0 #>   sys.child #> 1         0 #> 2         0   DFOP_SFO <- mkinmod(     parent = mkinsub(\"DFOP\", \"m1\"),     m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded   benchmark(     analytical = mkinfit(DFOP_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE),     deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE),     replications = 2) #>         test replications elapsed relative user.self sys.self user.child #> 1 analytical            2   0.389    1.000     0.388        0          0 #> 2    deSolve            2   0.513    1.319     0.513        0          0 #>   sys.child #> 1         0 #> 2         0 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":null,"dir":"Reference","previous_headings":"","what":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"datasets extracted active substance evaluation dossier published EFSA. Kinetic evaluations shown datasets intended illustrate advance kinetic modelling. fact data results shown imply license use context pesticide  registrations, use data may constrained data protection regulations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"","code":"dimethenamid_2018"},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"mkindsg object grouping seven datasets meta information","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate behaviour Rev. 2 - November 2017 https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"R code used create data object installed package 'dataset_generation' directory. code, page numbers given specific pieces information comments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"","code":"print(dimethenamid_2018) #> <mkindsg> holding 7 mkinds objects #> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018  #> Occurrence of observed compounds $observed_n: #> DMTAP   M23   M27   M31  DMTA  #>     3     7     7     7     4  #> Time normalisation factors $f_time_norm: #> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938 #> Meta information $meta: #>                      study  usda_soil_type study_moisture_ref_type rel_moisture #> Calke        Unsworth 2014      Sandy loam                     pF2         1.00 #> Borstel  Staudenmaier 2009            Sand                     pF1         0.50 #> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75 #> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75 #> Flaach          König 1996 Sandy clay loam                     pF1         0.40 #> BBA 2.2         König 1995      Loamy sand                     pF1         0.40 #> BBA 2.3         König 1995      Sandy loam                     pF1         0.40 #>          study_ref_moisture temperature #> Calke                    NA          20 #> Borstel               23.00          20 #> Elliot 1              33.37          23 #> Elliot 2              33.37          23 #> Flaach                   NA          20 #> BBA 2.2                  NA          20 #> BBA 2.3                  NA          20 dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL # \\dontrun{ # We don't use DFOP for the parent compound, as this gives numerical # instabilities in the fits sfo_sfo3p <- mkinmod(  DMTA = mkinsub(\"SFO\", c(\"M23\", \"M27\", \"M31\")),  M23 = mkinsub(\"SFO\"),  M27 = mkinsub(\"SFO\"),  M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),  quiet = TRUE ) dmta_sfo_sfo3p_tc <- mmkin(list(\"SFO-SFO3+\" = sfo_sfo3p),   dmta_ds, error_model = \"tc\", quiet = TRUE) print(dmta_sfo_sfo3p_tc) #> <mmkin> object #> Status of individual fits: #>  #>            dataset #> model       Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot #>   SFO-SFO3+ OK    OK      OK     OK      OK      OK     #>  #> OK: No warnings # The default (test_log_parms = FALSE) gives an undue # influence of ill-defined rate constants that have # extremely small values: plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = FALSE)  # If we disregards ill-defined rate constants, the results # look more plausible, but the truth is likely to be in # between these variants plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE) # We can also specify a default value for the failing # log parameters, to mimic FOCUS guidance plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE,   default_log_parms = log(2)/1000) # As these attempts are not satisfying, we use nonlinear mixed-effects models # f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc) # nlme reaches maxIter = 50 without convergence f_dmta_saem_tc <- saem(dmta_sfo_sfo3p_tc) # I am commenting out the convergence plot as rendering them # with pkgdown fails (at least without further tweaks to the # graphics device used) #saemix::plot(f_dmta_saem_tc$so, plot.type = \"convergence\") summary(f_dmta_saem_tc) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.0  #> Date of fit:     Mon May 12 20:21:22 2025  #> Date of summary: Mon May 12 20:21:22 2025  #>  #> Equations: #> d_DMTA/dt = - k_DMTA * DMTA #> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23 #> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * M27 + k_M31 * M31 #> d_M31/dt = + f_DMTA_to_M31 * k_DMTA * DMTA - k_M31 * M31 #>  #> Data: #> 563 observations of 4 variable(s) grouped in 6 datasets #>  #> Model predictions using solution type deSolve  #>  #> Fitted in 303.226 s #> Using 300, 100 iterations and 9 chains #>  #> Variance model: Two-component variance function  #>  #> Starting values for degradation parameters: #>       DMTA_0   log_k_DMTA    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1  #>      95.5662      -2.9048      -3.8130      -4.1600      -4.1486       0.1341  #> f_DMTA_ilr_2 f_DMTA_ilr_3  #>       0.1385      -1.6700  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>              DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 #> DMTA_0        4.802     0.0000    0.0000     0.000    0.0000       0.0000 #> log_k_DMTA    0.000     0.9834    0.0000     0.000    0.0000       0.0000 #> log_k_M23     0.000     0.0000    0.6983     0.000    0.0000       0.0000 #> log_k_M27     0.000     0.0000    0.0000     1.028    0.0000       0.0000 #> log_k_M31     0.000     0.0000    0.0000     0.000    0.9841       0.0000 #> f_DMTA_ilr_1  0.000     0.0000    0.0000     0.000    0.0000       0.7185 #> f_DMTA_ilr_2  0.000     0.0000    0.0000     0.000    0.0000       0.0000 #> f_DMTA_ilr_3  0.000     0.0000    0.0000     0.000    0.0000       0.0000 #>              f_DMTA_ilr_2 f_DMTA_ilr_3 #> DMTA_0             0.0000       0.0000 #> log_k_DMTA         0.0000       0.0000 #> log_k_M23          0.0000       0.0000 #> log_k_M27          0.0000       0.0000 #> log_k_M31          0.0000       0.0000 #> f_DMTA_ilr_1       0.0000       0.0000 #> f_DMTA_ilr_2       0.7378       0.0000 #> f_DMTA_ilr_3       0.0000       0.4451 #>  #> Starting values for error model parameters: #> a.1 b.1  #>   1   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>    AIC  BIC logLik #>   2276 2273  -1120 #>  #> Optimised parameters: #>                    est.   lower   upper #> DMTA_0          88.4862 84.1127 92.8598 #> log_k_DMTA      -3.0512 -3.5674 -2.5351 #> log_k_M23       -4.0576 -4.9013 -3.2139 #> log_k_M27       -3.8584 -4.2572 -3.4595 #> log_k_M31       -3.9779 -4.4844 -3.4714 #> f_DMTA_ilr_1     0.1264 -0.2186  0.4714 #> f_DMTA_ilr_2     0.1509 -0.2547  0.5565 #> f_DMTA_ilr_3    -1.3891 -1.6962 -1.0819 #> a.1              0.9196  0.8307  1.0085 #> b.1              0.1377  0.1205  0.1549 #> SD.DMTA_0        3.5956 -0.8167  8.0078 #> SD.log_k_DMTA    0.6437  0.2784  1.0091 #> SD.log_k_M23     0.9929  0.3719  1.6139 #> SD.log_k_M27     0.4530  0.1522  0.7537 #> SD.log_k_M31     0.5773  0.1952  0.9595 #> SD.f_DMTA_ilr_1  0.4063  0.1505  0.6621 #> SD.f_DMTA_ilr_2  0.4800  0.1817  0.7783 #> SD.f_DMTA_ilr_3  0.3582  0.1350  0.5814 #>  #> Correlation:  #>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2 #> log_k_DMTA    0.0306                                                     #> log_k_M23    -0.0234 -0.0032                                             #> log_k_M27    -0.0380 -0.0049  0.0041                                     #> log_k_M31    -0.0247 -0.0031  0.0022  0.0817                             #> f_DMTA_ilr_1 -0.0046 -0.0006  0.0425 -0.0438  0.0319                     #> f_DMTA_ilr_2 -0.0008 -0.0002  0.0216 -0.0267 -0.0890 -0.0349             #> f_DMTA_ilr_3 -0.1805 -0.0136  0.0434  0.0791  0.0390 -0.0061    0.0053   #>  #> Random effects: #>                   est.   lower  upper #> SD.DMTA_0       3.5956 -0.8167 8.0078 #> SD.log_k_DMTA   0.6437  0.2784 1.0091 #> SD.log_k_M23    0.9929  0.3719 1.6139 #> SD.log_k_M27    0.4530  0.1522 0.7537 #> SD.log_k_M31    0.5773  0.1952 0.9595 #> SD.f_DMTA_ilr_1 0.4063  0.1505 0.6621 #> SD.f_DMTA_ilr_2 0.4800  0.1817 0.7783 #> SD.f_DMTA_ilr_3 0.3582  0.1350 0.5814 #>  #> Variance model: #>       est.  lower  upper #> a.1 0.9196 0.8307 1.0085 #> b.1 0.1377 0.1205 0.1549 #>  #> Backtransformed parameters: #>                   est.     lower    upper #> DMTA_0        88.48621 84.112654 92.85977 #> k_DMTA         0.04730  0.028230  0.07926 #> k_M23          0.01729  0.007437  0.04020 #> k_M27          0.02110  0.014162  0.03144 #> k_M31          0.01872  0.011283  0.03107 #> f_DMTA_to_M23  0.14551        NA       NA #> f_DMTA_to_M27  0.12169        NA       NA #> f_DMTA_to_M31  0.11062        NA       NA #>  #> Resulting formation fractions: #>               ff #> DMTA_M23  0.1455 #> DMTA_M27  0.1217 #> DMTA_M31  0.1106 #> DMTA_sink 0.6222 #>  #> Estimated disappearance times: #>       DT50   DT90 #> DMTA 14.65  48.68 #> M23  40.09 133.17 #> M27  32.85 109.11 #> M31  37.02 122.97 # As the confidence interval for the random effects of DMTA_0 # includes zero, we could try an alternative model without # such random effects # f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc, #   covariance.model = diag(c(0, rep(1, 7)))) # saemix::plot(f_dmta_saem_tc_2$so, plot.type = \"convergence\") # This does not perform better judged by AIC and BIC # saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","title":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","text":"R code used create data object installed package 'dataset_generation' directory.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","text":"","code":"# \\dontrun{   sfo_mmkin <- mmkin(\"SFO\", ds_sfo, quiet = TRUE, error_model = \"tc\", cores = 15)   sfo_saem <- saem(sfo_mmkin, no_random_effect = \"parent_0\")   plot(sfo_saem)  # }  # This is the code used to generate the datasets cat(readLines(system.file(\"dataset_generation/ds_mixed.R\", package = \"mkin\")), sep = \"\\n\") #> # Synthetic data for hierarchical kinetic models #> # Refactored version of the code previously in tests/testthat/setup_script.R #> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it #> # is always 15 for consistency #>  #> library(mkin)  # We use mkinmod and mkinpredict #> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) #> n <- 15 #> log_sd <- 0.3 #> err_1 = list(const = 1, prop = 0.05) #> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) #> const <- function(value) 2 #>  #> set.seed(123456) #> SFO <- mkinmod(parent = mkinsub(\"SFO\")) #> sfo_pop <- list(parent_0 = 100, k_parent = 0.03) #> sfo_parms <- as.matrix(data.frame( #>     k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd))) #> set.seed(123456) #> ds_sfo <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(SFO, sfo_parms[i, ], #>     c(parent = sfo_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_sfo, \"pop\") <- sfo_pop #> attr(ds_sfo, \"parms\") <- sfo_parms #>  #> set.seed(123456) #> FOMC <- mkinmod(parent = mkinsub(\"FOMC\")) #> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) #> fomc_parms <- as.matrix(data.frame( #>     alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), #>     beta = rlnorm(n, log(fomc_pop$beta), 0.2))) #> set.seed(123456) #> ds_fomc <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], #>     c(parent = fomc_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_fomc, \"pop\") <- fomc_pop #> attr(ds_fomc, \"parms\") <- fomc_parms #>  #> set.seed(123456) #> DFOP <- mkinmod(parent = mkinsub(\"DFOP\")) #> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) #> dfop_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(dfop_pop$k1), log_sd), #>   k2 = rlnorm(n, log(dfop_pop$k2), log_sd), #>   g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) #> set.seed(123456) #> ds_dfop <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], #>     c(parent = dfop_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_dfop, \"pop\") <- dfop_pop #> attr(ds_dfop, \"parms\") <- dfop_parms #>  #> set.seed(123456) #> HS <- mkinmod(parent = mkinsub(\"HS\")) #> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) #> hs_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(hs_pop$k1), log_sd), #>   k2 = rlnorm(n, log(hs_pop$k2), log_sd), #>   tb = rlnorm(n, log(hs_pop$tb), 0.1))) #> set.seed(123456) #> ds_hs <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(HS, hs_parms[i, ], #>     c(parent = hs_pop$parent_0), sampling_times) #>   add_err(ds_mean, const, n = 1)[[1]] #> }) #> attr(ds_hs, \"pop\") <- hs_pop #> attr(ds_hs, \"parms\") <- hs_parms #>  #> set.seed(123456) #> DFOP_SFO <- mkinmod( #>   parent = mkinsub(\"DFOP\", \"m1\"), #>   m1 = mkinsub(\"SFO\"), #>   quiet = TRUE) #> dfop_sfo_pop <- list(parent_0 = 100, #>   k_m1 = 0.007, f_parent_to_m1 = 0.5, #>   k1 = 0.1, k2 = 0.02, g = 0.5) #> dfop_sfo_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd), #>   k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd), #>   g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)), #>   f_parent_to_m1 = plogis(rnorm(n, #>       qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), #>   k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd))) #> ds_dfop_sfo_mean <- lapply(1:n, #>   function(i) { #>     mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ], #>       c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times) #>   } #> ) #> set.seed(123456) #> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) { #>   add_err(ds, #>     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), #>     n = 1, secondary = \"m1\")[[1]] #> }) #> attr(ds_dfop_sfo, \"pop\") <- dfop_sfo_pop #> attr(ds_dfop_sfo, \"parms\") <- dfop_sfo_parms #>  #> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = \"data/ds_mixed.rda\", version = 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"function calculates DT50 DT90 values well formation fractions kinetic models fitted mkinfit. SFORB model specified one parents metabolites, Eigenvalues returned. equivalent rate constants DFOP model, advantage SFORB model can also used metabolites.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"","code":"endpoints(fit, covariates = NULL, covariate_quantile = 0.5)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"fit object class mkinfit, nlme.mmkin saem.mmkin, another object list components mkinmod containing mkinmod degradation model, two numeric vectors, bparms.optim bparms.fixed, contain parameter values model. covariates Numeric vector covariate values variables covariate models object. given, overrides 'covariate_quantile'. covariate_quantile argument effect fitted object covariate models. , default show endpoints median covariate values (50th percentile).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"list matrix dissipation times named distimes, , applicable, vector formation fractions named ff , SFORB model use, vector eigenvalues SFORB models, equivalent DFOP rate constants","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"Additional DT50 values calculated FOMC DT90 k1 k2 HS DFOP, well Eigenvalues b1 b2 SFORB models","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"function used internally summary.mkinfit, summary.nlme.mmkin summary.saem.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)   endpoints(fit) #> $distimes #>            DT50    DT90 DT50back #> parent 1.785233 15.1479 4.559973 #>    # \\dontrun{     fit_2 <- mkinfit(\"DFOP\", FOCUS_2006_C, quiet = TRUE)     endpoints(fit_2) #> $distimes #>            DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent 1.886925 21.25106 6.397207 1.508293 38.83438 #>      fit_3 <- mkinfit(\"SFORB\", FOCUS_2006_C, quiet = TRUE)     endpoints(fit_3) #> $ff #> parent_free  #>           1  #>  #> $SFORB #> parent_b1 parent_b2  parent_g  #> 0.4595574 0.0178488 0.8539454  #>  #> $distimes #>            DT50     DT90 DT50back DT50_parent_b1 DT50_parent_b2 #> parent 1.886925 21.25106 6.397208       1.508293       38.83438 #>    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":null,"dir":"Reference","previous_headings":"","what":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"12 datasets extracted active substance evaluation dossiers published  EFSA. Kinetic evaluations shown datasets intended illustrate  advance error model specifications. fact data  results shown imply license use context  pesticide registrations, use data may constrained  data protection regulations. Preprocessing data performed based recommendations FOCUS  kinetics workgroup (FOCUS, 2014) described . Datasets 1 2 Renewal Assessment Report (RAR) imazamox  (France, 2015, p. 15). setting values reported zero, LOQ 0.1  assumed. Metabolite residues reported day zero added  parent compound residues. Datasets 3 4 Renewal Assessment Report (RAR) isofetamid  (Belgium, 2014, p. 8) show data two different radiolabels.  dataset 4, value given metabolite day zero sampling  replicate B added parent compound, following respective  FOCUS recommendation. Dataset 5 Renewal Assessment Report (RAR) ethofumesate  (Austria, 2015, p. 16). Datasets 6 10 Renewal Assessment Report (RAR) glyphosate  (Germany, 2013, pages 8, 28, 50, 51). initial sampling,  residues given metabolite added parent  value, following recommendation FOCUS kinetics workgroup. Dataset 11 Renewal Assessment Report (RAR) 2,4-D  (Hellas, 2013, p. 644). Values reported zero set NA,  exception day three sampling metabolite A2, set  one half LOD reported 1% AR. Dataset 12 Renewal Assessment Report (RAR) thifensulfuron-methyl  (United Kingdom, 2014, p. 81).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"","code":"experimental_data_for_UBA_2019"},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"list containing twelve datasets R6 class defined mkinds,   containing, among others, following components title name dataset, e.g. Soil 1 data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 () Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 () France (2015). Imazamox Draft Renewal Assessment Report Volume 3 Annex B.8 () FOCUS (2014) “Generic guidance Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   Version 1.1, 18 December 2014   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics Germany (2013). Renewal Assessment Report Glyphosate Volume 3 Annex B.8: Environmental Fate   Behaviour Hellas (2013). Renewal Assessment Report 2,4-D Volume 3 Annex B.8: Fate behaviour   environment Ranke (2019) Documentation results obtained error model expertise   written German Umweltbundesamt. United Kingdom (2014). Thifensulfuron-methyl - Annex B.8 (Volume 3) Report Proposed   Decision United Kingdom made European Commission Regulation (EC) .   1141/2010 renewal active substance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"","code":"# \\dontrun{  # Model definitions sfo_sfo <- mkinmod(   parent = mkinsub(\"SFO\", to = \"A1\"),   A1 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  dfop_sfo <- mkinmod(   parent = mkinsub(\"DFOP\", to = \"A1\"),   A1 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  sfo_sfo_sfo <- mkinmod(   parent = mkinsub(\"SFO\", to = \"A1\"),   A1 = mkinsub(\"SFO\", to = \"A2\"),   A2 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  dfop_sfo_sfo <- mkinmod(   parent = mkinsub(\"DFOP\", to = \"A1\"),   A1 = mkinsub(\"SFO\", to = \"A2\"),   A2 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded d_1_2 <- lapply(experimental_data_for_UBA_2019[1:2], function(x) x$data) names(d_1_2) <- paste(\"Soil\", 1:2)   f_1_2_tc <- mmkin(list(\"DFOP-SFO-SFO\" = dfop_sfo_sfo), d_1_2, error_model = \"tc\")  plot(f_1_2_tc, resplot = \"errmod\")   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"Time step normalisation factors aerobic soil degradation described Appendix 8 FOCUS kinetics guidance (FOCUS 2014, p. 369).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"","code":"f_time_norm_focus(object, ...)  # S3 method for class 'numeric' f_time_norm_focus(   object,   moisture = NA,   field_moisture = NA,   temperature = object,   Q10 = 2.58,   walker = 0.7,   f_na = NA,   ... )  # S3 method for class 'mkindsg' f_time_norm_focus(   object,   study_moisture_ref_source = c(\"auto\", \"meta\", \"focus\"),   Q10 = 2.58,   walker = 0.7,   f_na = NA,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"object object containing information used calculations ... Currently used moisture Numeric vector moisture contents \\% w/w field_moisture Numeric vector moisture contents field capacity (pF2) \\% w/w temperature Numeric vector temperatures °C Q10 Q10 value used temperature normalisation walker Walker exponent used moisture normalisation f_na factor use NA values. set NA, factors complete cases returned. study_moisture_ref_source Source reference value used calculate study moisture. 'auto', preference given reference moisture given meta information, otherwise focus soil moisture soil class used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"","code":"f_time_norm_focus(25, 20, 25) # 1.37, compare FOCUS 2014 p. 184 #> [1] 1.373956  D24_2014$meta #>                                 study usda_soil_type study_moisture_ref_type #> Mississippi                Cohen 1991      Silt loam                    <NA> #> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1 #> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1 #> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1 #> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1 #>             rel_moisture temperature #> Mississippi           NA          25 #> Fayette              0.5          20 #> RefSol 03-G          0.5          20 #> Site E1              0.5          20 #> Site I2              0.5          20 # No moisture normalisation in the first dataset, so we use f_na = 1 to get # temperature only normalisation as in the EU evaluation f_time_norm_focus(D24_2014, study_moisture_ref_source = \"focus\", f_na = 1) #> $f_time_norm was (re)set to normalised values"},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":null,"dir":"Reference","previous_headings":"","what":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"value transcribed p. 36. table assumes field capacity corresponds pF2, MWHC pF 1 1/3 bar pF 2.5.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"","code":"focus_soil_moisture"},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"matrix upper case USDA soil classes row names, water tension ('pF1', 'pF2', 'pF 2.5') column names","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"Anonymous (2014) Generic Guidance Tier 1 FOCUS Ground Water Assessment Version 2.2, May 2014 https://esdac.jrc.ec.europa.eu/projects/ground-water","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"","code":"focus_soil_moisture #>                 pF1 pF2 pF2.5 #> Sand             24  12     7 #> Loamy sand       24  14     9 #> Sandy loam       27  19    15 #> Sandy clay loam  28  22    18 #> Clay loam        32  28    25 #> Loam             31  25    21 #> Silt loam        32  26    21 #> Silty clay loam  34  30    27 #> Silt             31  27    21 #> Sandy clay       41  35    31 #> Silty clay       44  40    36 #> Clay             53  48    43"},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve a degradation function from the mmkin namespace — get_deg_func","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"Retrieve degradation function mmkin namespace","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"","code":"get_deg_func()"},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"function likely previously assigned within nlme.mmkin","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical kinetics template — hierarchical_kinetics","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"R markdown format setting hierarchical kinetics based template provided mkin package. format based rmarkdown::pdf_document. Chunk options adapted. Echoing R code code chunks caching turned per default. character prepending output code chunks set empty string, code tidying , figure alignment defaults centering, positioning figures set \"H\", means figures move around document, stay user includes .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"","code":"hierarchical_kinetics(..., keep_tex = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"... Arguments rmarkdown::pdf_document keep_tex Keep intermediate tex file used conversion PDF. Note argument control whether keep auxiliary files (e.g., .aux) generated LaTeX compiling .tex .pdf. keep files, may set options(tinytex.clean = FALSE).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"R Markdown output format pass render","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"latter feature (positioning figures \"H\") depends LaTeX package 'float'. addition, LaTeX package 'listing' used template showing model fit summaries Appendix. means LaTeX packages 'float' 'listing' need installed TeX distribution used. Windows, easiest way achieve (TeX distribution present ) install 'tinytex' R package, run 'tinytex::install_tinytex()' get basic tiny Tex distribution, run 'tinytex::tlmgr_install(c(\"float\", \"listing\"))'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"","code":"# \\dontrun{ library(rmarkdown) # The following is now commented out after the relase of v1.2.3 for the generation # of online docs, as the command creates a directory and opens an editor #draft(\"example_analysis.rmd\", template = \"hierarchical_kinetics\", package = \"mkin\") # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get the names of ill-defined parameters — illparms","title":"Method to get the names of ill-defined parameters — illparms","text":"method generalised nonlinear regression fits obtained mkinfit mmkin checks degradation parameters pass Wald test (degradation kinetics often simply called t-test) significant difference zero. test, parameterisation without parameter transformations used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get the names of ill-defined parameters — illparms","text":"","code":"illparms(object, ...)  # S3 method for class 'mkinfit' illparms(object, conf.level = 0.95, ...)  # S3 method for class 'illparms.mkinfit' print(x, ...)  # S3 method for class 'mmkin' illparms(object, conf.level = 0.95, ...)  # S3 method for class 'illparms.mmkin' print(x, ...)  # S3 method for class 'saem.mmkin' illparms(   object,   conf.level = 0.95,   random = TRUE,   errmod = TRUE,   slopes = TRUE,   ... )  # S3 method for class 'illparms.saem.mmkin' print(x, ...)  # S3 method for class 'mhmkin' illparms(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...)  # S3 method for class 'illparms.mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get the names of ill-defined parameters — illparms","text":"object object investigate ... potential future extensions conf.level confidence level checking p values x object printed random hierarchical fits, random effects tested? errmod hierarchical fits, error model parameters tested? slopes hierarchical saem fits using saemix backend, slope parameters covariate model(starting 'beta_') tested?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get the names of ill-defined parameters — illparms","text":"mkinfit saem objects, character vector parameter names. mmkin mhmkin objects, matrix like object class 'illparms.mmkin' 'illparms.mhmkin'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Method to get the names of ill-defined parameters — illparms","text":"method hierarchical model fits, also known nonlinear mixed-effects model fits obtained saem mhmkin checks confidence intervals random effects expressed standard deviations include zero, confidence intervals error model parameters include zero.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Method to get the names of ill-defined parameters — illparms","text":"return objects printing methods. single fits, printing output anything case ill-defined parameters found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Method to get the names of ill-defined parameters — illparms","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_A, quiet = TRUE) #> Warning: Optimisation did not converge: #> false convergence (8) illparms(fit) #> [1] \"parent_0\" \"alpha\"    \"beta\"     \"sigma\"    # \\dontrun{ fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS C\" = FOCUS_2006_C),   quiet = TRUE) illparms(fits) #>       dataset #> model  FOCUS A                      FOCUS C #>   SFO                                       #>   FOMC parent_0, alpha, beta, sigma         # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to perform isometric log-ratio transformation — ilr","title":"Function to perform isometric log-ratio transformation — ilr","text":"implementation special case class isometric log-ratio transformations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to perform isometric log-ratio transformation — ilr","text":"","code":"ilr(x)  invilr(x)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to perform isometric log-ratio transformation — ilr","text":"x numeric vector. Naturally, forward transformation sensible vectors elements greater zero.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to perform isometric log-ratio transformation — ilr","text":"result forward backward transformation. returned components always sum 1 case inverse log-ratio transformation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to perform isometric log-ratio transformation — ilr","text":"Peter Filzmoser, Karel Hron (2008) Outlier Detection Compositional Data Using Robust Methods. Math Geosci 40 233-248","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to perform isometric log-ratio transformation — ilr","text":"René Lehmann Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to perform isometric log-ratio transformation — ilr","text":"","code":"# Order matters ilr(c(0.1, 1, 10)) #> [1] -1.628174 -2.820079 ilr(c(10, 1, 0.1)) #> [1] 1.628174 2.820079 # Equal entries give ilr transformations with zeros as elements ilr(c(3, 3, 3)) #> [1] 0 0 # Almost equal entries give small numbers ilr(c(0.3, 0.4, 0.3)) #> [1] -0.2034219  0.1174457 # Only the ratio between the numbers counts, not their sum invilr(ilr(c(0.7, 0.29, 0.01))) #> [1] 0.70 0.29 0.01 invilr(ilr(2.1 * c(0.7, 0.29, 0.01))) #> [1] 0.70 0.29 0.01 # Inverse transformation of larger numbers gives unequal elements invilr(-10) #> [1] 7.213536e-07 9.999993e-01 invilr(c(-10, 0)) #> [1] 7.207415e-07 9.991507e-01 8.486044e-04 # The sum of the elements of the inverse ilr is 1 sum(invilr(c(-10, 0))) #> [1] 1 # This is why we do not need all elements of the inverse transformation to go back: a <- c(0.1, 0.3, 0.5) b <- invilr(a) length(b) # Four elements #> [1] 4 ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5) #> [1] 0.1 0.3 0.5"},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"Confidence intervals parameters saem.mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' intervals(object, level = 0.95, backtransform = TRUE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"object fitted saem.mmkin object level confidence level. Must default 0.95 available saemix object backtransform case model fitted mkin transformations, backtransform parameters one one correlation transformed backtransformed parameters exists? ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"object 'intervals.saem.mmkin' 'intervals.lme' class attribute","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the distribution of log likelihoods from multistart objects — llhist","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"Produces histogram log-likelihoods. addition, likelihood original fit shown red vertical line.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"","code":"llhist(object, breaks = \"Sturges\", lpos = \"topleft\", main = \"\", ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"object multistart object breaks Passed hist lpos Positioning legend. main Title plot ... Passed hist","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":null,"dir":"Reference","previous_headings":"","what":"Lack-of-fit test for models fitted to data with replicates — loftest","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"generic function method currently defined mkinfit objects. fits anova model data contained object compares likelihoods using likelihood ratio test lrtest.default lmtest package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"","code":"loftest(object, ...)  # S3 method for class 'mkinfit' loftest(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"object model object defined loftest method ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"anova model interpreted simplest form mkinfit model, assuming constant variance means, enforcing structure means, one model parameter every mean replicate samples.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"","code":"# \\dontrun{ test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == \"parent\") sfo_fit <- mkinfit(\"SFO\", test_data, quiet = TRUE) plot_res(sfo_fit) # We see a clear pattern in the residuals  loftest(sfo_fit)  # We have a clear lack of fit #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1  10 -40.710                          #> 2   3 -63.954 -7 46.487  7.027e-08 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # # We try a different model (the one that was used to generate the data) dfop_fit <- mkinfit(\"DFOP\", test_data, quiet = TRUE) plot_res(dfop_fit) # We don't see systematic deviations, but heteroscedastic residuals  # therefore we should consider adapting the error model, although we have loftest(dfop_fit) # no lack of fit #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: DFOP with error model const #>   #Df  LogLik Df Chisq Pr(>Chisq) #> 1  10 -40.710                     #> 2   5 -42.453 -5 3.485     0.6257 # # This is the anova model used internally for the comparison test_data_anova <- test_data test_data_anova$time <- as.factor(test_data_anova$time) anova_fit <- lm(value ~ time, data = test_data_anova) summary(anova_fit) #>  #> Call: #> lm(formula = value ~ time, data = test_data_anova) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -6.1000 -0.5625  0.0000  0.5625  6.1000  #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept)  103.150      2.323  44.409 7.44e-12 *** #> time1        -19.950      3.285  -6.073 0.000185 *** #> time3        -50.800      3.285 -15.465 8.65e-08 *** #> time7        -68.500      3.285 -20.854 6.28e-09 *** #> time14       -79.750      3.285 -24.278 1.63e-09 *** #> time28       -86.000      3.285 -26.181 8.35e-10 *** #> time60       -94.900      3.285 -28.891 3.48e-10 *** #> time90       -98.500      3.285 -29.986 2.49e-10 *** #> time120     -100.450      3.285 -30.580 2.09e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #> Residual standard error: 3.285 on 9 degrees of freedom #> Multiple R-squared:  0.9953,\tAdjusted R-squared:  0.9912  #> F-statistic: 240.5 on 8 and 9 DF,  p-value: 1.417e-09 #>  logLik(anova_fit) # We get the same likelihood and degrees of freedom #> 'log Lik.' -40.71015 (df=10) # test_data_2 <- synthetic_data_for_UBA_2014[[12]]$data m_synth_SFO_lin <- mkinmod(parent = list(type = \"SFO\", to = \"M1\"),   M1 = list(type = \"SFO\", to = \"M2\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded sfo_lin_fit <- mkinfit(m_synth_SFO_lin, test_data_2, quiet = TRUE) plot_res(sfo_lin_fit) # not a good model, we try parallel formation  loftest(sfo_lin_fit) #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq)     #> 1  28  -93.606                           #> 2   7 -171.927 -21 156.64  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # m_synth_SFO_par <- mkinmod(parent = list(type = \"SFO\", to = c(\"M1\", \"M2\")),   M1 = list(type = \"SFO\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded sfo_par_fit <- mkinfit(m_synth_SFO_par, test_data_2, quiet = TRUE) plot_res(sfo_par_fit) # much better for metabolites  loftest(sfo_par_fit) #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq)     #> 1  28  -93.606                           #> 2   7 -156.331 -21 125.45  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # m_synth_DFOP_par <- mkinmod(parent = list(type = \"DFOP\", to = c(\"M1\", \"M2\")),   M1 = list(type = \"SFO\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded dfop_par_fit <- mkinfit(m_synth_DFOP_par, test_data_2, quiet = TRUE) plot_res(dfop_par_fit) # No visual lack of fit  loftest(dfop_par_fit)  # no lack of fit found by the test #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq) #> 1  28  -93.606                       #> 2   9 -102.763 -19 18.313     0.5016 # # The anova model used for comparison in the case of transformation products test_data_anova_2 <- dfop_par_fit$data test_data_anova_2$variable <- as.factor(test_data_anova_2$variable) test_data_anova_2$time <- as.factor(test_data_anova_2$time) anova_fit_2 <- lm(observed ~ time:variable - 1, data = test_data_anova_2) summary(anova_fit_2) #>  #> Call: #> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -6.1000 -0.5875  0.0000  0.5875  6.1000  #>  #> Coefficients: (2 not defined because of singularities) #>                        Estimate Std. Error t value Pr(>|t|)     #> time0:variableparent    103.150      1.573  65.562  < 2e-16 *** #> time1:variableparent     83.200      1.573  52.882  < 2e-16 *** #> time3:variableparent     52.350      1.573  33.274  < 2e-16 *** #> time7:variableparent     34.650      1.573  22.024  < 2e-16 *** #> time14:variableparent    23.400      1.573  14.873 6.35e-14 *** #> time28:variableparent    17.150      1.573  10.901 5.47e-11 *** #> time60:variableparent     8.250      1.573   5.244 1.99e-05 *** #> time90:variableparent     4.650      1.573   2.956 0.006717 **  #> time120:variableparent    2.700      1.573   1.716 0.098507 .   #> time0:variableM1             NA         NA      NA       NA     #> time1:variableM1         11.850      1.573   7.532 6.93e-08 *** #> time3:variableM1         22.700      1.573  14.428 1.26e-13 *** #> time7:variableM1         33.050      1.573  21.007  < 2e-16 *** #> time14:variableM1        31.250      1.573  19.863  < 2e-16 *** #> time28:variableM1        18.900      1.573  12.013 7.02e-12 *** #> time60:variableM1         7.550      1.573   4.799 6.28e-05 *** #> time90:variableM1         3.850      1.573   2.447 0.021772 *   #> time120:variableM1        2.050      1.573   1.303 0.204454     #> time0:variableM2             NA         NA      NA       NA     #> time1:variableM2          6.700      1.573   4.259 0.000254 *** #> time3:variableM2         16.750      1.573  10.646 8.93e-11 *** #> time7:variableM2         25.800      1.573  16.399 6.89e-15 *** #> time14:variableM2        28.600      1.573  18.178 6.35e-16 *** #> time28:variableM2        25.400      1.573  16.144 9.85e-15 *** #> time60:variableM2        21.600      1.573  13.729 3.81e-13 *** #> time90:variableM2        17.800      1.573  11.314 2.51e-11 *** #> time120:variableM2       14.100      1.573   8.962 2.79e-09 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #> Residual standard error: 2.225 on 25 degrees of freedom #> Multiple R-squared:  0.9979,\tAdjusted R-squared:  0.9957  #> F-statistic: 469.2 on 25 and 25 DF,  p-value: < 2.2e-16 #>  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"function returns product likelihood densities observed value, calculated part fitting procedure using dnorm, .e. assuming normal distribution, means predicted degradation model, standard deviations predicted error model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"","code":"# S3 method for class 'mkinfit' logLik(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"object object class mkinfit. ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"object class logLik number estimated parameters (degradation model parameters plus variance model parameters) attribute.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"total number estimated parameters returned value likelihood calculated sum fitted degradation model parameters fitted error model parameters.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"","code":"# \\dontrun{   sfo_sfo <- mkinmod(     parent = mkinsub(\"SFO\", to = \"m1\"),     m1 = mkinsub(\"SFO\")   ) #> Temporary DLL for differentials generated and loaded   d_t <- subset(FOCUS_2006_D, value != 0)   f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)   f_obs <- update(f_nw, error_model = \"obs\")   f_tc <- update(f_nw, error_model = \"tc\")   AIC(f_nw, f_obs, f_tc) #>       df      AIC #> f_nw   5 204.4486 #> f_obs  6 205.8727 #> f_tc   6 141.9656   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"logLik method for saem.mmkin objects — logLik.saem.mmkin","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"logLik method saem.mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' logLik(object, ..., method = c(\"is\", \"lin\", \"gq\"))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"object fitted saem.mmkin object ... Passed saemix::logLik.SaemixObject method Passed saemix::logLik.SaemixObject","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic kinetics — logistic.solution","title":"Logistic kinetics — logistic.solution","text":"Function describing exponential decline defined starting value, increasing rate constant, supposedly caused microbial growth","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic kinetics — logistic.solution","text":"","code":"logistic.solution(t, parent_0, kmax, k0, r)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic kinetics — logistic.solution","text":"t Time. parent_0 Starting value response variable time zero. kmax Maximum rate constant. k0 Minimum rate constant effective time zero. r Growth rate increase rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic kinetics — logistic.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logistic kinetics — logistic.solution","text":"solution logistic model reduces SFO.solution k0 equal kmax.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Logistic kinetics — logistic.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logistic kinetics — logistic.solution","text":"","code":"# Reproduce the plot on page 57 of FOCUS (2014)   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),        from = 0, to = 100, ylim = c(0, 100),        xlab = \"Time\", ylab = \"Residue\")   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),        from = 0, to = 100, add = TRUE, lty = 2, col = 2)   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),        from = 0, to = 100, add = TRUE, lty = 3, col = 3)   plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),        from = 0, to = 100, add = TRUE, lty = 4, col = 4)   plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),        from = 0, to = 100, add = TRUE, lty = 5, col = 5)   legend(\"topright\", inset = 0.05,          legend = paste0(\"k0 = \", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),                          \", r = \", c(0.2, 0.4, 0.8, 0.2, 0.2)),          lty = 1:5, col = 1:5)     # Fit with synthetic data   logistic <- mkinmod(parent = mkinsub(\"logistic\"))    sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)   parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2)   d_logistic <- mkinpredict(logistic,     parms_logistic, c(parent = 100),     sampling_times)   d_2_1 <- add_err(d_logistic,     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),     n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]    m <- mkinfit(\"logistic\", d_2_1, quiet = TRUE)   plot_sep(m)    summary(m)$bpar #>              Estimate   se_notrans   t value       Pr(>t)        Lower #> parent_0 1.057896e+02 1.9023449604 55.610120 3.768360e-16 1.016451e+02 #> kmax     6.398190e-02 0.0143201030  4.467978 3.841828e-04 3.929235e-02 #> k0       1.612775e-04 0.0005866813  0.274898 3.940351e-01 5.846688e-08 #> r        2.263946e-01 0.1718110664  1.317695 1.061043e-01 4.335843e-02 #> sigma    5.332935e+00 0.9145907310  5.830952 4.036926e-05 3.340213e+00 #>                Upper #> parent_0 109.9341588 #> kmax       0.1041853 #> k0         0.4448749 #> r          1.1821120 #> sigma      7.3256566   endpoints(m)$distimes #>            DT50     DT90  DT50_k0 DT50_kmax #> parent 36.86533 62.41511 4297.853  10.83349"},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"Compare two mkinfit models based likelihood. two fitted mkinfit objects given arguments, checked fitted data. responsibility user make sure models nested, .e. one less degrees freedom can expressed fixing parameters .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"","code":"# S3 method for class 'mkinfit' lrtest(object, object_2 = NULL, ...)  # S3 method for class 'mmkin' lrtest(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"object mkinfit object, mmkin column object containing two fits data. object_2 Optionally, another mkinfit object fitted data. ... Argument mkinfit, passed update.mkinfit creating alternative fitted object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"Alternatively, argument mkinfit can given passed update.mkinfit obtain alternative model. comparison made lrtest.default method lmtest package. model higher number fitted parameters (alternative hypothesis) listed first, model lower number fitted parameters (null hypothesis).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"","code":"# \\dontrun{ test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == \"parent\") sfo_fit <- mkinfit(\"SFO\", test_data, quiet = TRUE) dfop_fit <- mkinfit(\"DFOP\", test_data, quiet = TRUE) lrtest(dfop_fit, sfo_fit) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   3 -63.954 -2 43.002  4.594e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 lrtest(sfo_fit, dfop_fit) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   3 -63.954 -2 43.002  4.594e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  # The following two examples are commented out as they fail during # generation of the static help pages by pkgdown #lrtest(dfop_fit, error_model = \"tc\") #lrtest(dfop_fit, fixed_parms = c(k2 = 0))  # However, this equivalent syntax also works for static help pages lrtest(dfop_fit, update(dfop_fit, error_model = \"tc\")) #> Likelihood ratio test #>  #> Model 1: DFOP with error model tc #> Model 2: DFOP with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -34.587                          #> 2   5 -42.453 -1 15.731  7.302e-05 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 lrtest(dfop_fit, update(dfop_fit, fixed_parms = c(k2 = 0))) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: DFOP with error model const and fixed parameter(s) k2 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   4 -57.340 -1 29.776  4.851e-08 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"function calculates maximum moving window time weighted average concentrations (TWAs) kinetic models fitted mkinfit. Currently, calculations parent implemented SFO, FOMC, DFOP HS models, using analytical formulas given PEC soil section FOCUS guidance.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"","code":"max_twa_parent(fit, windows)  max_twa_sfo(M0 = 1, k, t)  max_twa_fomc(M0 = 1, alpha, beta, t)  max_twa_dfop(M0 = 1, k1, k2, g, t)  max_twa_hs(M0 = 1, k1, k2, tb, t)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"fit object class mkinfit. windows width time windows TWAs calculated. M0 initial concentration maximum time weighted average decline curve calculated. default use value 1, means relative maximum time weighted average factor (f_twa) calculated. k rate constant case SFO kinetics. t width time window. alpha Parameter FOMC model. beta Parameter FOMC model. k1 first rate constant DFOP HS kinetics. k2 second rate constant DFOP HS kinetics. g Parameter DFOP model. tb Parameter HS model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"max_twa_parent, numeric vector, named using windows argument.  functions, numeric vector length one (also known 'number').","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)   max_twa_parent(fit, c(7, 21)) #>        7       21  #> 34.71343 18.22124"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"Time course 2,4,5-trichlorophenoxyacetic acid, corresponding  2,4,5-trichlorophenol 2,4,5-trichloroanisole recovered diethylether  extracts.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"","code":"mccall81_245T"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"dataframe containing following variables. name name compound observed. Note T245 used                        acronym 2,4,5-T. T245 legitimate object name                        R, necessary specifying models using                        mkinmod. time numeric vector containing sampling times days                        treatment value numeric vector containing concentrations percent applied radioactivity soil factor containing name soil","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"McCall P, Vrona SA, Kelley SS (1981) Fate uniformly carbon-14 ring labelled 2,4,5-Trichlorophenoxyacetic acid 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107   doi:10.1021/jf00103a026","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"","code":"SFO_SFO_SFO <- mkinmod(T245 = list(type = \"SFO\", to = \"phenol\"),     phenol = list(type = \"SFO\", to = \"anisole\"),     anisole = list(type = \"SFO\")) #> Temporary DLL for differentials generated and loaded   # \\dontrun{     fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == \"Commerce\"), quiet = TRUE) #> Warning: Observations with value of zero were removed from the data     summary(fit.1)$bpar #>                         Estimate   se_notrans   t value       Pr(>t) #> T245_0              1.038550e+02 2.1847074943 47.537272 4.472189e-18 #> k_T245              4.337042e-02 0.0018983965 22.845818 2.276911e-13 #> k_phenol            4.050581e-01 0.2986993738  1.356073 9.756990e-02 #> k_anisole           6.678742e-03 0.0008021439  8.326114 2.623177e-07 #> f_T245_to_phenol    6.227599e-01 0.3985340721  1.562626 6.949414e-02 #> f_phenol_to_anisole 1.000000e+00 0.6718440131  1.488441 7.867790e-02 #> sigma               2.514628e+00 0.4907558973  5.123989 6.233159e-05 #>                            Lower        Upper #> T245_0              99.246061490 1.084640e+02 #> k_T245               0.039631621 4.746194e-02 #> k_phenol             0.218013879 7.525762e-01 #> k_anisole            0.005370739 8.305299e-03 #> f_T245_to_phenol     0.547559080 6.924813e-01 #> f_phenol_to_anisole  0.000000000 1.000000e+00 #> sigma                1.706607296 3.322649e+00     endpoints(fit.1) #> $ff #>    T245_phenol      T245_sink phenol_anisole    phenol_sink  #>   6.227599e-01   3.772401e-01   1.000000e+00   3.072478e-10  #>  #> $distimes #>               DT50      DT90 #> T245     15.982025  53.09114 #> phenol    1.711229   5.68458 #> anisole 103.784093 344.76329 #>      # formation fraction from phenol to anisol is practically 1. As we cannot     # fix formation fractions when using the ilr transformation, we can turn of     # the sink in the model generation     SFO_SFO_SFO_2 <- mkinmod(T245 = list(type = \"SFO\", to = \"phenol\"),       phenol = list(type = \"SFO\", to = \"anisole\", sink = FALSE),       anisole = list(type = \"SFO\")) #> Temporary DLL for differentials generated and loaded     fit.2 <- mkinfit(SFO_SFO_SFO_2, subset(mccall81_245T, soil == \"Commerce\"),       quiet = TRUE) #> Warning: Observations with value of zero were removed from the data     summary(fit.2)$bpar #>                      Estimate   se_notrans   t value       Pr(>t)        Lower #> T245_0           1.038550e+02 2.1623653059 48.028439 4.993108e-19 99.271020328 #> k_T245           4.337042e-02 0.0018343666 23.643268 3.573556e-14  0.039650976 #> k_phenol         4.050582e-01 0.1177237651  3.440752 1.679255e-03  0.218746589 #> k_anisole        6.678742e-03 0.0006829745  9.778903 1.872894e-08  0.005377083 #> f_T245_to_phenol 6.227599e-01 0.0342197873 18.198824 2.039411e-12  0.547975634 #> sigma            2.514628e+00 0.3790944250  6.633250 2.875782e-06  1.710983655 #>                         Upper #> T245_0           108.43904079 #> k_T245             0.04743877 #> k_phenol           0.75005593 #> k_anisole          0.00829550 #> f_T245_to_phenol   0.69212307 #> sigma              3.31827222     endpoints(fit.1) #> $ff #>    T245_phenol      T245_sink phenol_anisole    phenol_sink  #>   6.227599e-01   3.772401e-01   1.000000e+00   3.072478e-10  #>  #> $distimes #>               DT50      DT90 #> T245     15.982025  53.09114 #> phenol    1.711229   5.68458 #> anisole 103.784093 344.76329 #>      plot_sep(fit.2)    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"Calculate mean degradation parameters mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"","code":"mean_degparms(   object,   random = FALSE,   test_log_parms = FALSE,   conf.level = 0.6,   default_log_parms = NA )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"object mmkin row object containing several fits model different datasets random list fixed random effects returned? test_log_parms TRUE, log parameters considered mean calculations untransformed counterparts (likely rate constants) pass t-test significant difference zero. conf.level Possibility adjust required confidence level parameter tested requested 'test_log_parms'. default_log_parms set numeric value, used default value tested log parameters failed t-test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"random FALSE (default), named vector containing mean values fitted degradation model parameters. random TRUE, list fixed random effects, format required start argument nlme case single grouping variable ds.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"name methods expresses (multiple) hierarchichal (also known multilevel) multicompartment kinetic models fitted. kinetic models nonlinear, can use various nonlinear mixed-effects model fitting functions.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"","code":"mhmkin(objects, ...)  # S3 method for class 'mmkin' mhmkin(objects, ...)  # S3 method for class 'list' mhmkin(   objects,   backend = \"saemix\",   algorithm = \"saem\",   no_random_effect = NULL,   ...,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL )  # S3 method for class 'mhmkin' x[i, j, ..., drop = FALSE]  # S3 method for class 'mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"objects list mmkin objects containing fits degradation models data, using different error models. Alternatively, single mmkin object containing fits several degradation models data ... arguments passed nonlinear mixed-effects model fitting function. backend backend used fitting. Currently, saemix supported algorithm algorithm used fitting (currently used) no_random_effect Default NULL passed saem. character vector supplied, passed calls saem, exclude random effects matching parameters. Alternatively, list character vectors object class illparms.mhmkin can specified. dimensions return object current call , .e. number rows must match number degradation models mmkin object(s), number columns must match number error models used mmkin object(s). cores number cores used multicore processing. used cluster argument NULL. Windows machines, cores > 1 supported, need use cluster argument use multiple logical processors. Per default, cores detected parallel::detectCores() used, except Windows default 1. cluster cluster returned makeCluster used parallel execution. x mhmkin object. Row index selecting fits specific models j Column index selecting fits specific datasets drop FALSE, method always returns mhmkin object, otherwise either list fit objects single fit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"two-dimensional array fit objects /try-errors can indexed using degradation model names first index (row index) error model names second index (column index), class attribute 'mhmkin'. object inheriting mhmkin.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"","code":"# \\dontrun{ # We start with separate evaluations of all the first six datasets with two # degradation models and two error models f_sep_const <- mmkin(c(\"SFO\", \"FOMC\"), ds_fomc[1:6], cores = 2, quiet = TRUE) f_sep_tc <- update(f_sep_const, error_model = \"tc\") # The mhmkin function sets up hierarchical degradation models aka # nonlinear mixed-effects models for all four combinations, specifying # uncorrelated random effects for all degradation parameters f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) status(f_saem_1) #>            error #> degradation const tc #>        SFO  OK    OK #>        FOMC OK    OK #>  #> OK: Fit terminated successfully # The 'illparms' function shows that in all hierarchical fits, at least # one random effect is ill-defined (the confidence interval for the # random effect expressed as standard deviation includes zero) illparms(f_saem_1) #>            error #> degradation const        tc                         #>        SFO  sd(parent_0) sd(parent_0)               #>        FOMC sd(log_beta) sd(parent_0), sd(log_beta) # Therefore we repeat the fits, excluding the ill-defined random effects f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) #>            error #> degradation const tc #>        SFO  OK    OK #>        FOMC OK    OK #>  #> OK: Fit terminated successfully illparms(f_saem_2) #>            error #> degradation const tc #>        SFO           #>        FOMC          # Model comparisons show that FOMC with two-component error is preferable, # and confirms our reduction of the default parameter model anova(f_saem_1) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     5 574.40 573.35 -282.20 #> SFO tc        6 543.72 542.47 -265.86 #> FOMC const    7 489.67 488.22 -237.84 #> FOMC tc       8 406.11 404.44 -195.05 anova(f_saem_2) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     4 572.22 571.39 -282.11 #> SFO tc        5 541.63 540.59 -265.81 #> FOMC const    6 487.38 486.13 -237.69 #> FOMC tc       6 402.12 400.88 -195.06 # The convergence plot for the selected model looks fine saemix::plot(f_saem_2[[\"FOMC\", \"tc\"]]$so, plot.type = \"convergence\")  # The plot of predictions versus data shows that we have a pretty data-rich # situation with homogeneous distribution of residuals, because we used the # same degradation model, error model and parameter distribution model that # was used in the data generation. plot(f_saem_2[[\"FOMC\", \"tc\"]])  # We can specify the same parameter model reductions manually no_ranef <- list(\"parent_0\", \"log_beta\", \"parent_0\", c(\"parent_0\", \"log_beta\")) dim(no_ranef) <- c(2, 2) f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) anova(f_saem_2m) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     4 572.22 571.39 -282.11 #> SFO tc        5 541.63 540.59 -265.81 #> FOMC const    6 487.38 486.13 -237.69 #> FOMC tc       6 402.12 400.88 -195.06 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a mixed effects model from an mmkin row object — mixed","title":"Create a mixed effects model from an mmkin row object — mixed","text":"Create mixed effects model mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a mixed effects model from an mmkin row object — mixed","text":"","code":"mixed(object, ...)  # S3 method for class 'mmkin' mixed(object, method = c(\"none\"), ...)  # S3 method for class 'mixed.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a mixed effects model from an mmkin row object — mixed","text":"object mmkin row object ... Currently used method method used x mixed.mmkin object print digits Number digits use printing.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a mixed effects model from an mmkin row object — mixed","text":"object class 'mixed.mmkin' observed data single dataframe convenient plotting","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a mixed effects model from an mmkin row object — mixed","text":"","code":"sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) n_biphasic <- 8 err_1 = list(const = 1, prop = 0.07)  DFOP_SFO <- mkinmod(   parent = mkinsub(\"DFOP\", \"m1\"),   m1 = mkinsub(\"SFO\"),   quiet = TRUE)  set.seed(123456) log_sd <- 0.3 syn_biphasic_parms <- as.matrix(data.frame(   k1 = rlnorm(n_biphasic, log(0.05), log_sd),   k2 = rlnorm(n_biphasic, log(0.01), log_sd),   g = plogis(rnorm(n_biphasic, 0, log_sd)),   f_parent_to_m1 = plogis(rnorm(n_biphasic, 0, log_sd)),   k_m1 = rlnorm(n_biphasic, log(0.002), log_sd)))  ds_biphasic_mean <- lapply(1:n_biphasic,   function(i) {     mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ],       c(parent = 100, m1 = 0), sampling_times)   } )  set.seed(123456L) ds_biphasic <- lapply(ds_biphasic_mean, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),     n = 1, secondary = \"m1\")[[1]] })  # \\dontrun{ f_mmkin <- mmkin(list(\"DFOP-SFO\" = DFOP_SFO), ds_biphasic, error_model = \"tc\", quiet = TRUE)  f_mixed <- mixed(f_mmkin) print(f_mixed) #> Kinetic model fitted by nonlinear regression to each dataset #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 271 observations of 2 variable(s) grouped in 8 datasets #>  #> <mmkin> object #> Status of individual fits: #>  #>           dataset #> model      1  2  3  4  5  6 7  8  #>   DFOP-SFO OK OK OK OK OK C OK OK #>  #> C: Optimisation did not converge: #> iteration limit reached without convergence (10) #> OK: No warnings #>  #> Mean fitted parameters: #>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2  #>      100.605312       -8.758664       -0.001917       -3.350887       -3.990017  #>        g_qlogis  #>       -0.091167  plot(f_mixed)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a dataframe from long to wide format — mkin_long_to_wide","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"function takes dataframe long form, .e. row observed value, converts dataframe one independent variable several dependent variables columns.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"","code":"mkin_long_to_wide(long_data, time = \"time\", outtime = \"time\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"long_data dataframe must contain one variable called \"time\" time values specified time argument, one column called \"name\" grouping observed values, finally one column observed values called \"value\". time name time variable long input data. outtime name time variable wide output data.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"Dataframe wide format.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"","code":"mkin_long_to_wide(FOCUS_2006_D) #>    time parent    m1 #> 1     0  99.46  0.00 #> 2     0 102.04  0.00 #> 3     1  93.50  4.84 #> 4     1  92.50  5.64 #> 5     3  63.23 12.91 #> 6     3  68.99 12.96 #> 7     7  52.32 22.97 #> 8     7  55.13 24.47 #> 9    14  27.27 41.69 #> 10   14  26.64 33.21 #> 11   21  11.50 44.37 #> 12   21  11.64 46.44 #> 13   35   2.85 41.22 #> 14   35   2.91 37.95 #> 15   50   0.69 41.19 #> 16   50   0.63 40.01 #> 17   75   0.05 40.09 #> 18   75   0.06 33.85 #> 19  100     NA 31.04 #> 20  100     NA 33.13 #> 21  120     NA 25.15 #> 22  120     NA 33.31"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"function simply takes dataframe one independent variable several dependent variable converts long form required mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"","code":"mkin_wide_to_long(wide_data, time = \"t\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"wide_data dataframe must contain one variable time values specified time argument usually one column observed values. time name time variable.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"Dataframe long format needed mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"","code":"wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5)) mkin_wide_to_long(wide) #>   name time value #> 1    x    1     1 #> 2    x    2     4 #> 3    x    3     7 #> 4    y    1     3 #> 5    y    2     4 #> 6    y    3     5"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":null,"dir":"Reference","previous_headings":"","what":"A dataset class for mkin — mkinds","title":"A dataset class for mkin — mkinds","text":"moment dataset class hardly used mkin. example, mkinfit take mkinds datasets argument, works dataframes contained data field mkinds objects. datasets provided package come mkinds objects nevertheless.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"# S3 method for class 'mkinds' print(x, data = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"x mkinds object. data data printed? ... used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"A dataset class for mkin — mkinds","text":"title full title dataset sampling_times sampling times time_unit time unit observed Names observed variables unit unit observations replicates maximum number replicates per sampling time data data frame least columns name, time value order compatible mkinfit","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"A dataset class for mkin — mkinds","text":"mkinds$new() mkinds$clone()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"A dataset class for mkin — mkinds","text":"Create new mkinds object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"mkinds$new(title = \"\", data, time_unit = NA, unit = NA)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"title dataset title data data time_unit time unit unit unit observations","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"A dataset class for mkin — mkinds","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"mkinds$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A dataset class for mkin — mkinds","text":"","code":"mds <- mkinds$new(\"FOCUS A\", FOCUS_2006_A) print(mds) #> <mkinds> with $title:  FOCUS A  #> Observed compounds $observed:  parent  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 62, 90, 118  #> With a maximum of  1  replicates"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":null,"dir":"Reference","previous_headings":"","what":"A class for dataset groups for mkin — mkindsg","title":"A class for dataset groups for mkin — mkindsg","text":"container working datasets share least one compound, combined evaluations desirable. Time normalisation factors initialised value 1 dataset data supplied.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"# S3 method for class 'mkindsg' print(x, data = FALSE, verbose = data, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"x mkindsg object. data mkinds objects printed data? verbose mkinds objects printed? ... used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"A class for dataset groups for mkin — mkindsg","text":"title title dataset group ds list mkinds objects observed_n Occurrence counts compounds datasets f_time_norm Time normalisation factors meta data frame row dataset, containing additional information form categorical data (factors) numerical data (e.g. temperature, moisture, covariates like soil pH).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"A class for dataset groups for mkin — mkindsg","text":"mkindsg$new() mkindsg$clone()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"A class for dataset groups for mkin — mkindsg","text":"Create new mkindsg object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mkindsg$new(title = \"\", ds, f_time_norm = rep(1, length(ds)), meta)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"title title ds list mkinds objects f_time_norm Time normalisation factors meta meta data","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"A class for dataset groups for mkin — mkindsg","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mkindsg$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mdsg <- mkindsg$new(\"Experimental X\", experimental_data_for_UBA_2019[6:10]) print(mdsg) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  print(mdsg, verbose = TRUE) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  #>  #> Datasets $ds: #> <mkinds> with $title:  Soil 6  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 7  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 60, 90, 120, 180  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 8  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 9  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 10  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 8, 14, 21, 41, 63, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  print(mdsg, verbose = TRUE, data = TRUE) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  #>  #> Datasets $ds: #> <mkinds> with $title:  Soil 6  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   97.2   NA #> 2     0   96.4   NA #> 3     3   71.1  4.3 #> 4     3   69.2  4.6 #> 5     6   58.1  7.0 #> 6     6   56.6  7.2 #> 7    10   44.4  8.2 #> 8    10   43.4  8.0 #> 9    20   33.3 11.0 #> 10   20   29.2 13.7 #> 11   34   17.6 11.5 #> 12   34   18.0 12.7 #> 13   55   10.5 14.9 #> 14   55    9.3 14.5 #> 15   90    4.5 12.1 #> 16   90    4.7 12.3 #> 17  112    3.0  9.9 #> 18  112    3.4 10.2 #> 19  132    2.3  8.8 #> 20  132    2.7  7.8 #>  #> <mkinds> with $title:  Soil 7  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 60, 90, 120, 180  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   93.6   NA #> 2     0   92.3   NA #> 3     3   87.0  3.9 #> 4     3   82.2  3.1 #> 5     7   74.0  6.9 #> 6     7   73.9  6.6 #> 7    14   64.2 10.4 #> 8    14   69.5  8.3 #> 9    30   54.0 14.4 #> 10   30   54.6 13.7 #> 11   60   41.1 22.1 #> 12   60   38.4 22.3 #> 13   90   32.5 27.5 #> 14   90   35.5 25.4 #> 15  120   28.1 28.0 #> 16  120   29.0 26.6 #> 17  180   26.5 25.8 #> 18  180   27.6 25.3 #>  #> <mkinds> with $title:  Soil 8  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   91.9   NA #> 2     0   90.8   NA #> 3     1   64.9  9.6 #> 4     1   66.2  7.7 #> 5     3   43.5 15.0 #> 6     3   44.1 15.1 #> 7     8   18.3 21.2 #> 8     8   18.1 21.1 #> 9    14   10.2 19.7 #> 10   14   10.8 18.9 #> 11   27    4.9 17.5 #> 12   27    3.3 15.9 #> 13   48    1.6  9.5 #> 14   48    1.5  9.8 #> 15   70    1.1  6.2 #> 16   70    0.9  6.1 #>  #> <mkinds> with $title:  Soil 9  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   99.8   NA #> 2     0   98.3   NA #> 3     1   77.1  4.2 #> 4     1   77.2  3.9 #> 5     3   59.0  7.4 #> 6     3   58.1  7.9 #> 7     8   27.4 14.5 #> 8     8   29.2 13.7 #> 9    14   19.1 14.2 #> 10   14   29.6 12.2 #> 11   27   10.1 13.7 #> 12   27   18.2 13.2 #> 13   48    4.5 13.6 #> 14   48    9.1 15.4 #> 15   70    2.3 10.4 #> 16   70    2.9 11.6 #> 17   91    2.0 10.0 #> 18   91    1.8  9.5 #> 19  120    2.0  9.1 #> 20  120    2.2  9.0 #>  #> <mkinds> with $title:  Soil 10  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 8, 14, 21, 41, 63, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   96.1   NA #> 2     0   94.3   NA #> 3     8   73.9  3.3 #> 4     8   73.9  3.4 #> 5    14   69.4  3.9 #> 6    14   73.1  2.9 #> 7    21   65.6  6.4 #> 8    21   65.3  7.2 #> 9    41   55.9  9.1 #> 10   41   54.4  8.5 #> 11   63   47.0 11.7 #> 12   63   49.3 12.0 #> 13   91   44.7 13.3 #> 14   91   46.7 13.2 #> 15  120   42.1 14.3 #> 16  120   41.3 12.1"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"function finds smallest relative error still resulting passing chi-squared test defined FOCUS kinetics report 2006.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"","code":"mkinerrmin(fit, alpha = 0.05)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"fit object class mkinfit. alpha confidence level chosen chi-squared test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"dataframe following components: err.min relative error, expressed fraction. n.optim number optimised parameters attributed data series. df number remaining degrees freedom chi2 error level calculations.  Note mean values used chi2 statistic therefore every time point observed values series counts one time. dataframe one row total dataset one row observed state variable model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"function used internally summary.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"","code":"SFO_SFO = mkinmod(parent = mkinsub(\"SFO\", to = \"m1\"),                   m1 = mkinsub(\"SFO\"),                   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data round(mkinerrmin(fit_FOCUS_D), 4) #>          err.min n.optim df #> All data  0.0640       4 15 #> parent    0.0646       2  7 #> m1        0.0469       2  8 # \\dontrun{   fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)   round(mkinerrmin(fit_FOCUS_E), 4) #>          err.min n.optim df #> All data  0.1544       4 13 #> parent    0.1659       2  7 #> m1        0.1095       2  6 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"function plots squared residuals specified subset observed variables mkinfit object. addition, one dashed line(s) show fitted error model.  combined plot fitted model error model plot can obtained plot.mkinfit using argument show_errplot = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"","code":"mkinerrplot(   object,   obs_vars = names(object$mkinmod$map),   xlim = c(0, 1.1 * max(object$data$predicted)),   xlab = \"Predicted\",   ylab = \"Squared residual\",   maxy = \"auto\",   legend = TRUE,   lpos = \"topright\",   col_obs = \"auto\",   pch_obs = \"auto\",   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"object fit represented mkinfit object. obs_vars character vector names observed variables residuals plotted. Defaults observed variables model xlim plot range x direction. xlab Label x axis. ylab Label y axis. maxy Maximum value residuals. used scaling y axis defaults \"auto\". legend legend plotted? lpos legend placed? Default \"topright\". passed legend. col_obs Colors observed variables. pch_obs Symbols used observed variables. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"","code":"# \\dontrun{ model <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, FOCUS_2006_D, error_model = \"tc\", quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinerrplot(fit)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a kinetic model to data with one or more state variables — mkinfit","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"function maximises likelihood observed data using Port algorithm stats::nlminb(), specified initial fixed parameters starting values.  step optimisation, kinetic model solved using function mkinpredict(), except analytical solution implemented, case model solved using degradation function mkinmod object. parameters selected error model fitted simultaneously degradation model parameters, arguments likelihood function.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"","code":"mkinfit(   mkinmod,   observed,   parms.ini = \"auto\",   state.ini = \"auto\",   err.ini = \"auto\",   fixed_parms = NULL,   fixed_initials = names(mkinmod$diffs)[-1],   from_max_mean = FALSE,   solution_type = c(\"auto\", \"analytical\", \"eigen\", \"deSolve\"),   method.ode = \"lsoda\",   use_compiled = \"auto\",   control = list(eval.max = 300, iter.max = 200),   transform_rates = TRUE,   transform_fractions = TRUE,   quiet = FALSE,   atol = 1e-08,   rtol = 1e-10,   error_model = c(\"const\", \"obs\", \"tc\"),   error_model_algorithm = c(\"auto\", \"d_3\", \"direct\", \"twostep\", \"threestep\", \"fourstep\",     \"IRLS\", \"OLS\"),   reweight.tol = 1e-08,   reweight.max.iter = 10,   trace_parms = FALSE,   test_residuals = FALSE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"mkinmod list class mkinmod, containing kinetic model fitted data, one shorthand names (\"SFO\", \"FOMC\", \"DFOP\", \"HS\", \"SFORB\", \"IORE\"). shorthand name given, parent degradation model generated variable highest value observed. observed dataframe object coercible dataframe (e.g. tibble) observed data.  first column called \"name\" must contain name observed variable data point. second column must contain times observation, named \"time\". third column must named \"value\" contain observed values. Zero values \"value\" column removed, warning, order avoid problems fitting two-component error model. expected problem, general, values zero observed degradation data, lower limit detection. parms.ini named vector initial values parameters, including parameters optimised potentially also fixed parameters indicated fixed_parms.  set \"auto\", initial values rate constants set default values.  Using parameter names model gives error. possible specify subset parameters model needs. can use parameter lists \"bparms.ode\" previously fitted model, contains differential equation parameters model.  works nicely models nested. example given . state.ini named vector initial values state variables model. case observed variables represented one model variable, names differ names observed variables (see map component mkinmod). default set initial value first model variable mean time zero values variable maximum observed value, others 0.  variable time zero observations, initial value set 100. err.ini named vector initial values error model parameters optimised.  set \"auto\", initial values set default values.  Otherwise, inital values error model parameters must given. fixed_parms names parameters optimised rather kept values specified parms.ini. Alternatively, named numeric vector parameters fixed, regardless values parms.ini. fixed_initials names model variables initial state time 0 excluded optimisation. Defaults state variables except first one. from_max_mean set TRUE, model one observed variable, data time maximum observed value (averaging sampling time) discarded, time subtracted remaining time values, time maximum observed mean value new time zero. solution_type set \"eigen\", solution system differential equations based spectral decomposition coefficient matrix cases possible. set \"deSolve\", numerical ode solver package deSolve used. set \"analytical\", analytical solution model used. implemented relatively simple degradation models.  default \"auto\", uses \"analytical\" possible, otherwise \"deSolve\" compiler present, \"eigen\" compiler present model can expressed using eigenvalues eigenvectors. method.ode solution method passed via mkinpredict() deSolve::ode() case solution type \"deSolve\". default \"lsoda\" performant, sometimes fails converge. use_compiled set FALSE, compiled version mkinmod model used calls mkinpredict() even compiled version present. control list control arguments passed stats::nlminb(). transform_rates Boolean specifying kinetic rate constants transformed model specification used fitting better compliance assumption normal distribution estimator. TRUE, also alpha beta parameters FOMC model log-transformed, well k1 k2 rate constants DFOP HS models break point tb HS model.  FALSE, zero used lower bound rates optimisation. transform_fractions Boolean specifying formation fractions transformed model specification used fitting better compliance assumption normal distribution estimator. default (TRUE) transformations. TRUE, g parameter DFOP model also transformed. Transformations described transform_odeparms. quiet Suppress printing current value negative log-likelihood improvement? atol Absolute error tolerance, passed deSolve::ode(). Default 1e-8, lower default deSolve::lsoda() function used per default. rtol Absolute error tolerance, passed deSolve::ode(). Default 1e-10, much lower deSolve::lsoda(). error_model error model \"const\", constant standard deviation assumed. error model \"obs\", observed variable assumed variance. error model \"tc\" (two-component error model), two component error model similar one described Rocke Lorenzato (1995) used setting likelihood function.  Note model deviates model Rocke Lorenzato, model implies errors follow lognormal distribution large values, normal distribution assumed method. error_model_algorithm \"auto\", selected algorithm depends error model.  error model \"const\", unweighted nonlinear least squares fitting (\"OLS\") selected. error model \"obs\", \"tc\", \"d_3\" algorithm selected. algorithm \"d_3\" directly minimize negative log-likelihood independently also use three step algorithm described . fit higher likelihood returned. algorithm \"direct\" directly minimize negative log-likelihood. algorithm \"twostep\" minimize negative log-likelihood initial unweighted least squares optimisation step. algorithm \"threestep\" starts unweighted least squares, optimizes error model using degradation model parameters found, minimizes negative log-likelihood free degradation error model parameters. algorithm \"fourstep\" starts unweighted least squares, optimizes error model using degradation model parameters found, optimizes degradation model fixed error model parameters, finally minimizes negative log-likelihood free degradation error model parameters. algorithm \"IRLS\" (Iteratively Reweighted Least Squares) starts unweighted least squares, iterates optimization error model parameters subsequent optimization degradation model using error model parameters, error model parameters converge. reweight.tol Tolerance convergence criterion calculated error model parameters IRLS fits. reweight.max.iter Maximum number iterations IRLS fits. trace_parms trace parameter values listed? test_residuals residuals tested normal distribution? ... arguments passed deSolve::ode().","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"list \"mkinfit\" class attribute.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Per default, parameters kinetic models internally transformed order better satisfy assumption normal distribution estimators.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"using \"IORE\" submodel metabolites, fitting \"transform_rates = TRUE\" (default) often leads failures numerical ODE solver. situation may help switch internal rate transformation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Rocke DM Lorenzato S (1995) two-component model measurement error analytical chemistry. Technometrics 37(2), 176-184. Ranke J Meinecke S (2019) Error Models Kinetic Evaluation Chemical Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124 .","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"","code":"# Use shorthand notation for parent only degradation fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE) summary(fit) #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:22:04 2025  #> Date of summary: Mon May 12 20:22:04 2025  #>  #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #>  #> Model predictions using solution type analytical  #>  #> Fitted using 222 model solutions performed in 0.027 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>          value   type #> parent_0  85.1  state #> alpha      1.0 deparm #> beta      10.0 deparm #>  #> Starting values for the transformed parameters actually optimised: #>               value lower upper #> parent_0  85.100000  -Inf   Inf #> log_alpha  0.000000  -Inf   Inf #> log_beta   2.302585  -Inf   Inf #>  #> Fixed parameter values: #> None #>  #> Results: #>  #>        AIC      BIC    logLik #>   44.68652 45.47542 -18.34326 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>           Estimate Std. Error    Lower   Upper #> parent_0  85.87000     1.8070 81.23000 90.5200 #> log_alpha  0.05192     0.1353 -0.29580  0.3996 #> log_beta   0.65100     0.2287  0.06315  1.2390 #> sigma      1.85700     0.4378  0.73200  2.9830 #>  #> Parameter correlation: #>             parent_0  log_alpha   log_beta     sigma #> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.681e-08 #> log_alpha -1.565e-01  1.000e+00  9.564e-01 1.013e-07 #> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.637e-08 #> sigma      4.681e-08  1.013e-07  8.637e-08 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   85.870  47.530 3.893e-08 81.2300 90.520 #> alpha       1.053   7.393 3.562e-04  0.7439  1.491 #> beta        1.917   4.373 3.601e-03  1.0650  3.451 #> sigma       1.857   4.243 4.074e-03  0.7320  2.983 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   6.657       3  6 #> parent     6.657       3  6 #>  #> Estimated disappearance times: #>         DT50  DT90 DT50back #> parent 1.785 15.15     4.56 #>  #> Data: #>  time variable observed predicted residual #>     0   parent     85.1    85.875  -0.7749 #>     1   parent     57.9    55.191   2.7091 #>     3   parent     29.9    31.845  -1.9452 #>     7   parent     14.6    17.012  -2.4124 #>    14   parent      9.7     9.241   0.4590 #>    28   parent      6.6     4.754   1.8460 #>    63   parent      4.0     2.102   1.8977 #>    91   parent      3.9     1.441   2.4590 #>   119   parent      0.6     1.092  -0.4919  # One parent compound, one metabolite, both single first order. # We remove zero values from FOCUS dataset D in order to avoid warnings FOCUS_D <- subset(FOCUS_2006_D, value != 0) # Use mkinsub for convenience in model formulation. Pathway to sink included per default. SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded  # Fit the model quietly to the FOCUS example dataset D using defaults fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) plot_sep(fit)  # As lower parent values appear to have lower variance, we try an alternative error model fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\") # This avoids the warning, and the likelihood ratio test confirms it is preferable lrtest(fit.tc, fit) #> Likelihood ratio test #>  #> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -64.983                          #> 2   5 -97.224 -1 64.483  9.737e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # We can also allow for different variances of parent and metabolite as error model fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"obs\") # The two-component error model has significantly higher likelihood lrtest(fit.obs, fit.tc) #> Likelihood ratio test #>  #> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -64.983                          #> 2   6 -96.936  0 63.907  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 parms(fit.tc) #>       parent_0       k_parent           k_m1 f_parent_to_m1      sigma_low  #>   1.007343e+02   1.005562e-01   5.166712e-03   5.083933e-01   3.049883e-03  #>       rsd_high  #>   7.928118e-02  endpoints(fit.tc) #> $ff #>   parent_m1 parent_sink  #>   0.5083933   0.4916067  #>  #> $distimes #>             DT50      DT90 #> parent   6.89313  22.89848 #> m1     134.15634 445.65771 #>   # We can show a quick (only one replication) benchmark for this case, as we # have several alternative solution methods for the model. We skip # uncompiled deSolve, as it is so slow. More benchmarks are found in the # benchmark vignette # \\dontrun{ if(require(rbenchmark)) {   benchmark(replications = 1, order = \"relative\", columns = c(\"test\", \"relative\", \"elapsed\"),     deSolve_compiled = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"deSolve\", use_compiled = TRUE),     eigen = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"eigen\"),     analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"analytical\")) } #>               test relative elapsed #> 3       analytical    1.000   0.256 #> 2            eigen    1.898   0.486 #> 1 deSolve_compiled    2.000   0.512 # }  # Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO # \\dontrun{ FOMC_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE) # Again, we get a warning and try a more sophisticated error model fit.FOMC_SFO.tc <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\") # This model has a higher likelihood, but not significantly so lrtest(fit.tc, fit.FOMC_SFO.tc) #> Likelihood ratio test #>  #> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq) #> 1   7 -64.829                      #> 2   6 -64.983 -1 0.3075     0.5792 # Also, the missing standard error for log_beta and the t-tests for alpha # and beta indicate overparameterisation summary(fit.FOMC_SFO.tc, data = FALSE) #> Warning: NaNs produced #> Warning: diag(V) had non-positive or NA entries; the non-finite result may be dubious #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:22:10 2025  #> Date of summary: Mon May 12 20:22:10 2025  #>  #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) * #>            parent - k_m1 * m1 #>  #> Model predictions using solution type deSolve  #>  #> Fitted using 4062 model solutions performed in 0.809 s #>  #> Error model: Two-component variance function  #>  #> Error model algorithm: d_3  #> Direct fitting and three-step fitting yield approximately the same likelihood  #>  #> Starting values for parameters to be optimised: #>                 value   type #> parent_0       100.75  state #> alpha            1.00 deparm #> beta            10.00 deparm #> k_m1             0.10 deparm #> f_parent_to_m1   0.50 deparm #> sigma_low        0.10  error #> rsd_high         0.10  error #>  #> Starting values for the transformed parameters actually optimised: #>                      value lower upper #> parent_0        100.750000  -Inf   Inf #> log_k_m1         -2.302585  -Inf   Inf #> f_parent_qlogis   0.000000  -Inf   Inf #> log_alpha         0.000000  -Inf   Inf #> log_beta          2.302585  -Inf   Inf #> sigma_low         0.100000     0   Inf #> rsd_high          0.100000     0   Inf #>  #> Fixed parameter values: #>      value  type #> m1_0     0 state #>  #> Results: #>  #>       AIC      BIC    logLik #>   143.658 155.1211 -64.82902 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>                   Estimate Std. Error     Lower      Upper #> parent_0        101.600000  2.6400000 96.240000 107.000000 #> log_k_m1         -5.284000  0.0929100 -5.474000  -5.095000 #> f_parent_qlogis   0.001426  0.0766900 -0.155000   0.157800 #> log_alpha         5.522000  0.0077320  5.506000   5.538000 #> log_beta          7.806000        NaN       NaN        NaN #> sigma_low         0.002488  0.0002431  0.001992   0.002984 #> rsd_high          0.079210  0.0093280  0.060180   0.098230 #>  #> Parameter correlation: #>                  parent_0  log_k_m1 f_parent_qlogis log_alpha log_beta #> parent_0         1.000000 -0.095161        -0.76675   0.70542      NaN #> log_k_m1        -0.095161  1.000000         0.51429  -0.14382      NaN #> f_parent_qlogis -0.766750  0.514286         1.00000  -0.61393      NaN #> log_alpha        0.705417 -0.143821        -0.61393   1.00000      NaN #> log_beta              NaN       NaN             NaN       NaN        1 #> sigma_low        0.016086  0.001583         0.01547   5.87036      NaN #> rsd_high         0.006618 -0.011695        -0.05356   0.04848      NaN #>                 sigma_low  rsd_high #> parent_0         0.016086  0.006618 #> log_k_m1         0.001583 -0.011695 #> f_parent_qlogis  0.015466 -0.053560 #> log_alpha        5.870361  0.048483 #> log_beta              NaN       NaN #> sigma_low        1.000000 -0.652545 #> rsd_high        -0.652545  1.000000 #>  #> 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       1.016e+02 32.7800 6.310e-26 9.624e+01 1.070e+02 #> k_m1           5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03 #> f_parent_to_m1 5.004e-01 20.8300 4.316e-20 4.613e-01 5.394e-01 #> alpha          2.502e+02  0.5624 2.889e-01 2.463e+02 2.542e+02 #> beta           2.455e+03  0.5549 2.915e-01        NA        NA #> sigma_low      2.488e-03  0.4843 3.158e-01 1.992e-03 2.984e-03 #> rsd_high       7.921e-02  8.4300 8.001e-10 6.018e-02 9.823e-02 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   6.781       5 14 #> parent     7.141       3  6 #> m1         4.640       2  8 #>  #> Resulting formation fractions: #>                 ff #> parent_m1   0.5004 #> parent_sink 0.4996 #>  #> Estimated disappearance times: #>           DT50  DT90 DT50back #> parent   6.812  22.7    6.834 #> m1     136.661 454.0       NA  # We can easily use starting parameters from the parent only fit (only for illustration) fit.FOMC = mkinfit(\"FOMC\", FOCUS_2006_D, quiet = TRUE, error_model = \"tc\") fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE,   parms.ini = fit.FOMC$bparms.ode, error_model = \"tc\") # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to set up a kinetic model with one or more state variables — mkinmod","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"function usually called using call mkinsub() observed variable, specifying corresponding submodel well outgoing pathways (see examples). Print mkinmod objects way user finds way get components.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"","code":"mkinmod(   ...,   use_of_ff = \"max\",   name = NULL,   speclist = NULL,   quiet = FALSE,   verbose = FALSE,   dll_dir = NULL,   unload = FALSE,   overwrite = FALSE )  # S3 method for class 'mkinmod' print(x, ...)  mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"... observed variable, list obtained mkinsub() specified argument (see examples).  Currently, single first order kinetics \"SFO\", indeterminate order rate equation kinetics \"IORE\", single first order reversible binding \"SFORB\" implemented variables, \"FOMC\", \"DFOP\", \"HS\" \"logistic\" can additionally chosen first variable assumed source compartment. Additionally, mkinsub() argument , specifying names variables transfer assumed model. argument use_of_ff set \"min\" model compartment \"SFO\" \"SFORB\", additional mkinsub() argument can sink = FALSE, effectively fixing flux sink zero. print.mkinmod, argument currently used. use_of_ff Specification use formation fractions model equations , applicable, coefficient matrix.  \"max\", formation fractions always used (default).  \"min\", minimum use formation fractions made, .e. first-order pathway metabolite rate constant. name name model. valid R object name. speclist specification observed variables submodel types pathways can given single list using argument. Default NULL. quiet messages suppressed? verbose TRUE, passed inline::cfunction() applicable give detailed information C function built. dll_dir Directory DLL object, generated internally inline::cfunction(), saved.  DLL stored permanent location use future sessions, 'dll_dir' 'name' specified. helpful fit objects cached e.g. knitr, cache remains functional across sessions DLL stored user defined location. unload DLL target location 'dll_dir' already loaded, unloaded first? overwrite file exists target DLL location 'dll_dir', overwritten? x mkinmod object. submodel Character vector length one specify submodel type. See mkinmod list allowed submodel names. Vector names state variable transformation shall included model. sink pathway sink included model addition pathways state variables? full_name optional name used e.g. plotting fits performed model.  can use non-ASCII characters , R code portable, .e. may produce unintended plot results operating systems system configurations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"list class mkinmod use mkinfit(), containing, among others, diffs vector string representations differential equations, one modelling variable. map list containing named character vectors observed variable, specifying modelling variables represented. use_of_ff content use_of_ff passed list component. deg_func generated, function containing solution degradation model. coefmat coefficient matrix, system differential equations can represented one. cf generated, compiled function calculating derivatives returned cfunction. list use mkinmod.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"definition model types parameters, equations given FOCUS NAFTA guidance documents used. kinetic models one observed variable, symbolic solution system differential equations included resulting mkinmod object cases, speeding solution. C compiler found pkgbuild::has_compiler() one observed variable specification, C code generated evaluating differential equations, compiled using inline::cfunction() added resulting mkinmod object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"IORE submodel well tested metabolites. using model metabolites, may want read note help page mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics NAFTA Technical Working Group Pesticides (dated) Guidance Evaluating Calculating Degradation Kinetics Environmental Media","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"","code":"# Specify the SFO model (this is not needed any more, as we can now mkinfit(\"SFO\", ...) SFO <- mkinmod(parent = mkinsub(\"SFO\"))  # One parent compound, one metabolite, both single first order SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(SFO_SFO) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $parent #> $type: SFO; $to: m1; $sink: TRUE #> $m1 #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_parent/dt = - k_parent * parent #> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1  # \\dontrun{  fit_sfo_sfo <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, solution_type = \"deSolve\") #> Warning: Observations with value of zero were removed from the data   # Now supplying compound names used for plotting, and write to user defined location  # We need to choose a path outside the session tempdir because this gets removed  DLL_dir <- \"~/.local/share/mkin\"  if (!dir.exists(DLL_dir)) dir.create(DLL_dir)  SFO_SFO.2 <- mkinmod(    parent = mkinsub(\"SFO\", \"m1\", full_name = \"Test compound\"),    m1 = mkinsub(\"SFO\", full_name = \"Metabolite M1\"),    name = \"SFO_SFO\", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE) #> Temporary DLL for differentials generated and loaded #> Copied DLL from /tmp/Rtmph4D0tb/file1385e349a06fa9.so to /home/jranke/.local/share/mkin/SFO_SFO.so # Now we can save the model and restore it in a new session saveRDS(SFO_SFO.2, file = \"~/SFO_SFO.rds\") # Terminate the R session here if you would like to check, and then do library(mkin) SFO_SFO.3 <- readRDS(\"~/SFO_SFO.rds\") fit_sfo_sfo <- mkinfit(SFO_SFO.3, FOCUS_2006_D, quiet = TRUE, solution_type = \"deSolve\") #> Warning: Observations with value of zero were removed from the data  # Show details of creating the C function SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\"), verbose = TRUE) #> Setting PKG_CPPFLAGS to -DR_NO_REMAP  #> Program source: #>   1: #include <R.h> #>   2:  #>   3:  #>   4: static double parms [3]; #>   5: #define k_parent parms[0] #>   6: #define f_parent_to_m1 parms[1] #>   7: #define k_m1 parms[2] #>   8:  #>   9: void initpar(void (* odeparms)(int *, double *)) { #>  10:     int N = 3; #>  11:     odeparms(&N, parms); #>  12: } #>  13:  #>  14:  #>  15: void diffs ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) { #>  16:  #>  17: f[0] = - k_parent * y[0]; #>  18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1]; #>  19: } #> Temporary DLL for differentials generated and loaded  # The symbolic solution which is available in this case is not # made for human reading but for speed of computation SFO_SFO$deg_func #> function (observed, odeini, odeparms)  #> { #>     predicted <- numeric(0) #>     with(as.list(odeparms), { #>         t <- observed[observed$name == \"parent\", \"time\"] #>         predicted <<- c(predicted, SFO.solution(t, odeini[\"parent\"],  #>             k_parent)) #>         t <- observed[observed$name == \"m1\", \"time\"] #>         predicted <<- c(predicted, (((k_m1 - k_parent) * odeini[\"m1\"] -  #>             f_parent_to_m1 * k_parent * odeini[\"parent\"]) * exp(-k_m1 *  #>             t) + f_parent_to_m1 * k_parent * odeini[\"parent\"] *  #>             exp(-k_parent * t))/(k_m1 - k_parent)) #>     }) #>     return(predicted) #> } #> <environment: 0x5555585b89c0>  # If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par <- mkinmod(  parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),  M1 = mkinsub(\"SFO\"),  M2 = mkinsub(\"SFO\"),  quiet = TRUE)  fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par,   synthetic_data_for_UBA_2014[[12]]$data,   quiet = TRUE) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"function plots confidence intervals parameters fitted using mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"","code":"mkinparplot(object)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"object fit represented mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"","code":"# \\dontrun{ model <- mkinmod(   T245 = mkinsub(\"SFO\", to = c(\"phenol\"), sink = FALSE),   phenol = mkinsub(\"SFO\", to = c(\"anisole\")),   anisole = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, subset(mccall81_245T, soil == \"Commerce\"), quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinparplot(fit)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"Deprecated function. now calls plot method plot.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"","code":"mkinplot(fit, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"fit object class mkinfit. ... arguments passed plot.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":null,"dir":"Reference","previous_headings":"","what":"Produce predictions from a kinetic model using specific parameters — mkinpredict","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"function produces time series observed variables kinetic model specified mkinmod, using specific set kinetic parameters initial values state variables.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"","code":"mkinpredict(x, odeparms, odeini, outtimes, ...)  # S3 method for class 'mkinmod' mkinpredict(   x,   odeparms = c(k_parent_sink = 0.1),   odeini = c(parent = 100),   outtimes = seq(0, 120, by = 0.1),   solution_type = \"deSolve\",   use_compiled = \"auto\",   use_symbols = FALSE,   method.ode = \"lsoda\",   atol = 1e-08,   rtol = 1e-10,   maxsteps = 20000L,   map_output = TRUE,   na_stop = TRUE,   ... )  # S3 method for class 'mkinfit' mkinpredict(   x,   odeparms = x$bparms.ode,   odeini = x$bparms.state,   outtimes = seq(0, 120, by = 0.1),   solution_type = \"deSolve\",   use_compiled = \"auto\",   method.ode = \"lsoda\",   atol = 1e-08,   rtol = 1e-10,   map_output = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"x kinetic model produced mkinmod, kinetic fit fitted mkinfit. latter case, fitted parameters used prediction. odeparms numeric vector specifying parameters used kinetic model, generally defined set ordinary differential equations. odeini numeric vector containing initial values state variables model. Note state variables can differ observed variables, example case SFORB model. outtimes numeric vector specifying time points model predictions generated. ... arguments passed ode solver case solver used. solution_type method used producing predictions. generally \"analytical\" one observed variable, usually \"deSolve\" case several observed variables. third possibility \"eigen\" fast comparison uncompiled ODE models, applicable models, e.g. using FOMC parent compound. use_compiled set FALSE, compiled version mkinmod model used, even present. use_symbols set TRUE (default), symbol info present mkinmod object used available accessing compiled code method.ode solution method passed via mkinpredict deSolve::ode() case solution type \"deSolve\" using compiled code. using compiled code, lsoda supported. atol Absolute error tolerance, passed ode solver. rtol Absolute error tolerance, passed ode solver. maxsteps Maximum number steps, passed ode solver. map_output Boolean specify output list values observed variables (default) state variables (set FALSE). Setting FALSE effect analytical solutions, always return mapped output. na_stop error deSolve::ode() returns NaN values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"matrix numeric solution wide format","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"","code":"SFO <- mkinmod(degradinol = mkinsub(\"SFO\")) # Compare solution types mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"analytical\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108997 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"deSolve\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108996 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"deSolve\", use_compiled = FALSE) #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108996 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"eigen\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108997 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752  # Compare integration methods to analytical solution mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"analytical\")[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"lsoda\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"ode45\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"rk4\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2480043  # rk4 is not as precise here  # The number of output times used to make a lot of difference until the # default for atol was adjusted mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),       seq(0, 20, by = 0.1))[201,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),       seq(0, 20, by = 0.01))[2001,] #>       time degradinol  #> 20.0000000  0.2478752   # Comparison of the performance of solution types SFO_SFO = mkinmod(parent = list(type = \"SFO\", to = \"m1\"),                   m1 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded if(require(rbenchmark)) {   benchmark(replications = 10, order = \"relative\", columns = c(\"test\", \"relative\", \"elapsed\"),     eigen = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"eigen\")[201,],     deSolve_compiled = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"deSolve\")[201,],     deSolve = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"deSolve\", use_compiled = FALSE)[201,],     analytical = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"analytical\", use_compiled = FALSE)[201,]) } #>               test relative elapsed #> 2 deSolve_compiled      1.0   0.002 #> 4       analytical      1.0   0.002 #> 1            eigen      4.5   0.009 #> 3          deSolve     35.0   0.070  # \\dontrun{   # Predict from a fitted model   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = \"deSolve\")   head(mkinpredict(f)) #> Error in !is.null(x$symbols) & use_symbols: operations are possible only for numeric, logical or complex types # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot residuals stored in an mkin object — mkinresplot","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"function plots residuals specified subset observed variables mkinfit object. combined plot fitted model residuals can obtained using plot.mkinfit using argument show_residuals = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"","code":"mkinresplot(   object,   obs_vars = names(object$mkinmod$map),   xlim = c(0, 1.1 * max(object$data$time)),   standardized = FALSE,   xlab = \"Time\",   ylab = ifelse(standardized, \"Standardized residual\", \"Residual\"),   maxabs = \"auto\",   legend = TRUE,   lpos = \"topright\",   col_obs = \"auto\",   pch_obs = \"auto\",   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"object fit represented mkinfit object. obs_vars character vector names observed variables residuals plotted. Defaults observed variables model xlim plot range x direction. standardized residuals standardized dividing standard deviation given error model fit? xlab Label x axis. ylab Label y axis. maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". legend legend plotted? lpos legend placed? Default \"topright\". passed legend. col_obs Colors observed variables. pch_obs Symbols used observed variables. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"Johannes Ranke Katrin Lindenberger","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"","code":"model <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinresplot(fit, \"m1\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"function calls mkinfit combinations models datasets specified first two arguments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"","code":"mmkin(   models = c(\"SFO\", \"FOMC\", \"DFOP\"),   datasets,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL,   ... )  # S3 method for class 'mmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"models Either character vector shorthand names like c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\", \"SFORB\"), optionally named list mkinmod objects. datasets optionally named list datasets suitable observed data mkinfit. cores number cores used multicore processing. used cluster argument NULL. Windows machines, cores > 1 supported, need use cluster argument use multiple logical processors. Per default, cores detected parallel::detectCores() used, except Windows default 1. cluster cluster returned makeCluster used parallel execution. ... used. x mmkin object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"two-dimensional array mkinfit objects /try-errors can indexed using model names first index (row index) dataset names second index (column index).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"","code":"# \\dontrun{ m_synth_SFO_lin <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                            M1 = mkinsub(\"SFO\", \"M2\"),                            M2 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_FOMC_lin <- mkinmod(parent = mkinsub(\"FOMC\", \"M1\"),                             M1 = mkinsub(\"SFO\", \"M2\"),                             M2 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin) datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data) names(datasets) <- paste(\"Dataset\", 1:3)  time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE)) time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))  time_default #>    user  system elapsed  #>   2.423   1.112   1.111  time_1 #>    user  system elapsed  #>   2.655   0.036   2.691   endpoints(fits.0[[\"SFO_lin\", 2]]) #> $ff #>   parent_M1 parent_sink       M1_M2     M1_sink  #>   0.7340481   0.2659519   0.7505690   0.2494310  #>  #> $distimes #>              DT50       DT90 #> parent  0.8777689   2.915885 #> M1      2.3257403   7.725942 #> M2     33.7201060 112.015767 #>   # plot.mkinfit handles rows or columns of mmkin result objects plot(fits.0[1, ])  plot(fits.0[1, ], obs_var = c(\"M1\", \"M2\"))  plot(fits.0[, 1])  # Use double brackets to extract a single mkinfit object, which will be plotted # by plot.mkinfit and can be plotted using plot_sep plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)  plot_sep(fits.0[[1, 1]]) # Plotting with mmkin (single brackets, extracting an mmkin object) does not # allow to plot the observed variables separately plot(fits.0[1, 1])   # On Windows, we can use multiple cores by making a cluster first cl <- parallel::makePSOCKcluster(12) f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"),   list(A = FOCUS_2006_A, B = FOCUS_2006_B, C = FOCUS_2006_C, D = FOCUS_2006_D),   cluster = cl, quiet = TRUE) print(f) #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  A  B  C  D  #>   SFO  OK OK OK OK #>   FOMC C  OK OK OK #>   DFOP OK OK OK OK #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings # We get false convergence for the FOMC fit to FOCUS_2006_A because this # dataset is really SFO, and the FOMC fit is overparameterised parallel::stopCluster(cl) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a hierarchical model fit with multiple starting values — multistart","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"purpose method check certain algorithm fitting nonlinear hierarchical models (also known nonlinear mixed-effects models) reliably yield results sufficiently similar , started certain range reasonable starting parameters. inspired article practical identifiabiliy frame nonlinear mixed-effects models Duchesne et al (2021).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"","code":"multistart(   object,   n = 50,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL,   ... )  # S3 method for class 'saem.mmkin' multistart(object, n = 50, cores = 1, cluster = NULL, ...)  # S3 method for class 'multistart' print(x, ...)  best(object, ...)  # Default S3 method best(object, ...)  which.best(object, ...)  # Default S3 method which.best(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"object fit object work n many different combinations starting parameters used? cores many fits run parallel (posix platforms)? cluster cluster returned parallel::makeCluster used parallel execution. ... Passed update function. x multistart object print","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"list saem.mmkin objects, class attributes 'multistart.saem.mmkin' 'multistart'. object highest likelihood index object highest likelihood","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"Duchesne R, Guillemin , Gandrillon O, Crauste F. Practical identifiability frame nonlinear mixed effects models: example vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. doi: 10.1186/s12859-021-04373-4.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"","code":"# \\dontrun{ library(mkin) dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- dmta_ds[[\"Elliot 2\"]] <- NULL  f_mmkin <- mmkin(\"DFOP\", dmta_ds, error_model = \"tc\", cores = 7, quiet = TRUE) f_saem_full <- saem(f_mmkin) f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16) parplot(f_saem_full_multi, lpos = \"topleft\", las = 2)  illparms(f_saem_full) #> [1] \"sd(log_k2)\"  f_saem_reduced <- update(f_saem_full, no_random_effect = \"log_k2\") illparms(f_saem_reduced) # On Windows, we need to create a PSOCK cluster first and refer to it # in the call to multistart() library(parallel) cl <- makePSOCKcluster(12) f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl) parplot(f_saem_reduced_multi, lpos = \"topright\", ylim = c(0.5, 2), las = 2)  stopCluster(cl) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate parent kinetics using the NAFTA guidance — nafta","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"function fits SFO, IORE DFOP models using mmkin returns object class nafta methods printing plotting. Print nafta objects. results three models printed order increasing model complexity, .e. SFO, IORE, finally DFOP.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"","code":"nafta(ds, title = NA, quiet = FALSE, ...)  # S3 method for class 'nafta' print(x, quiet = TRUE, digits = 3, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics environmental media. NAFTA Technical Working Group Pesticides https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance Calculate Representative Half-life Values Characterizing Pesticide Degradation https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"ds dataframe must contain one variable called \"time\" time values specified time argument, one column called \"name\" grouping observed values, finally one column observed values called \"value\". title Optional title dataset quiet evaluation text shown? ... arguments passed mmkin (printing method). x nafta object. digits Number digits used printing parameters dissipation times.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"list class nafta. list element named \"mmkin\" mmkin object containing fits three models. list element named \"title\" contains title dataset used. list element \"data\" contains dataset used fits.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"","code":"nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> 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   print(nafta_evaluation) #> Sums of squares: #>       SFO      IORE      DFOP  #> 1378.6832  615.7730  517.8836  #>  #> Critical sum of squares for checking the SFO model: #> [1] 717.4598 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)    Lower   Upper #> parent_0  83.7558 1.80e-14 77.18268 90.3288 #> k_parent   0.0017 7.43e-05  0.00112  0.0026 #> sigma      8.7518 1.22e-05  5.64278 11.8608 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       9.69e+01     NA 8.88e+01 1.05e+02 #> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09 #> N_parent       6.68e+00     NA 4.19e+00 9.17e+00 #> sigma          5.85e+00     NA 3.76e+00 7.94e+00 #>  #> $DFOP #>          Estimate   Pr(>t)    Lower    Upper #> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 #> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02 #> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03 #> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01 #> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00 #>  #>  #> DTx values: #>      DT50    DT90 DT50_rep #> SFO   407    1350      407 #> IORE  541 5190000  1560000 #> DFOP  429    2380      841 #>  #> Representative half-life: #> [1] 841.41   plot(nafta_evaluation)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions to create nlme models from mmkin row objects — nlme_function","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"functions facilitate setting nonlinear mixed effects model mmkin row object. mmkin row object essentially list mkinfit objects obtained fitting model list datasets. used internally nlme.mmkin() method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"","code":"nlme_function(object)  nlme_data(object)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"object mmkin row object containing several fits model different datasets","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"function can used nlme nlme::groupedData object","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"","code":"sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) m_SFO <- mkinmod(parent = mkinsub(\"SFO\")) d_SFO_1 <- mkinpredict(m_SFO,   c(k_parent = 0.1),   c(parent = 98), sampling_times) d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = \"time\") d_SFO_2 <- mkinpredict(m_SFO,   c(k_parent = 0.05),   c(parent = 102), sampling_times) d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = \"time\") d_SFO_3 <- mkinpredict(m_SFO,   c(k_parent = 0.02),   c(parent = 103), sampling_times) d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = \"time\")  d1 <- add_err(d_SFO_1, function(value) 3, n = 1) d2 <- add_err(d_SFO_2, function(value) 2, n = 1) d3 <- add_err(d_SFO_3, function(value) 4, n = 1) ds <- c(d1 = d1, d2 = d2, d3 = d3)  f <- mmkin(\"SFO\", ds, cores = 1, quiet = TRUE) mean_dp <- mean_degparms(f) grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by # pkgdown to generate the html docs. assign(\"nlme_f\", nlme_f, globalenv()) assign(\"grouped_data\", grouped_data, globalenv())  library(nlme) m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),   data = grouped_data,   fixed = parent_0 + log_k_parent_sink ~ 1,   random = pdDiag(parent_0 + log_k_parent_sink ~ 1),   start = mean_dp) summary(m_nlme) #> Nonlinear mixed-effects model fit by maximum likelihood #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink)  #>   Data: grouped_data  #>        AIC      BIC    logLik #>   266.6428 275.8935 -128.3214 #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #>  Level: ds #>  Structure: Diagonal #>             parent_0 log_k_parent_sink Residual #> StdDev: 0.0003775775         0.7058039 3.065183 #>  #> Fixed effects:  parent_0 + log_k_parent_sink ~ 1  #>                       Value Std.Error DF   t-value p-value #> parent_0          101.18323 0.7900461 43 128.07257       0 #> log_k_parent_sink  -3.08708 0.4171755 43  -7.39995       0 #>  Correlation:  #>                   prnt_0 #> log_k_parent_sink 0.031  #>  #> Standardized Within-Group Residuals: #>         Min          Q1         Med          Q3         Max  #> -2.38427070 -0.52059848  0.03593021  0.39987268  2.73188969  #>  #> Number of Observations: 47 #> Number of Groups: 3  plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))  # augPred does not work on fits with more than one state # variable # # The procedure is greatly simplified by the nlme.mmkin function f_nlme <- nlme(f) plot(f_nlme)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an nlme model for an mmkin row object — nlme.mmkin","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"functions sets nonlinear mixed effects model mmkin row object. mmkin row object essentially list mkinfit objects obtained fitting model list datasets.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"","code":"# S3 method for class 'mmkin' nlme(   model,   data = \"auto\",   fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =     paste(el, 1, sep = \"~\")))),   random = pdDiag(fixed),   groups,   start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),   correlation = NULL,   weights = NULL,   subset,   method = c(\"ML\", \"REML\"),   na.action = na.fail,   naPattern,   control = list(),   verbose = FALSE )  # S3 method for class 'nlme.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)  # S3 method for class 'nlme.mmkin' update(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"model mmkin row object. data Ignored, data taken mmkin model fixed Ignored, degradation parameters fitted mmkin model used fixed parameters random specified, correlations random effects set optimised degradation model parameters. achieved using nlme::pdDiag method. groups See documentation nlme start specified, mean values fitted degradation parameters taken mmkin object used correlation See documentation nlme weights passed nlme subset passed nlme method passed nlme na.action passed nlme naPattern passed nlme control passed nlme verbose passed nlme x nlme.mmkin object print digits Number digits use printing ... Update specifications passed update.nlme object nlme.mmkin object update","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"Upon success, fitted 'nlme.mmkin' object, nlme object additional elements. also inherits 'mixed.mmkin'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"Note convergence nlme algorithms depends quality data. degradation kinetics, often datasets (e.g. data soils) complicated degradation models, may make impossible obtain convergence nlme.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"object inherits nlme::nlme, wealth methods automatically work 'nlme.mmkin' objects, nlme::intervals(), nlme::anova.lme() nlme::coef.lme().","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"","code":"ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")], name == \"parent\"))  # \\dontrun{   f <- mmkin(c(\"SFO\", \"DFOP\"), ds, quiet = TRUE, cores = 1)   library(nlme)   f_nlme_sfo <- nlme(f[\"SFO\", ])   f_nlme_dfop <- nlme(f[\"DFOP\", ])   anova(f_nlme_sfo, f_nlme_dfop) #>             Model df      AIC      BIC    logLik   Test  L.Ratio p-value #> f_nlme_sfo      1  5 625.0539 637.5529 -307.5269                         #> f_nlme_dfop     2  9 495.1270 517.6253 -238.5635 1 vs 2 137.9269  <.0001   print(f_nlme_dfop) #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -238.6 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)  #> parent_0   log_k1   log_k2 g_qlogis  #>  94.1702  -1.8002  -4.1474   0.0324  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev:    2.488 0.8447   1.33   0.4652    2.321 #>    plot(f_nlme_dfop)    endpoints(f_nlme_dfop) #> $distimes #>            DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent 10.79857 100.7937 30.34192 4.193937 43.85442 #>     ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],    function(x) x$data[c(\"name\", \"time\", \"value\")])   m_sfo_sfo <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),     A1 = mkinsub(\"SFO\"), use_of_ff = \"min\", quiet = TRUE)   m_sfo_sfo_ff <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),     A1 = mkinsub(\"SFO\"), use_of_ff = \"max\", quiet = TRUE)   m_dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),     A1 = mkinsub(\"SFO\"), quiet = TRUE)    f_2 <- mmkin(list(\"SFO-SFO\" = m_sfo_sfo,    \"SFO-SFO-ff\" = m_sfo_sfo_ff,    \"DFOP-SFO\" = m_dfop_sfo),     ds_2, quiet = TRUE)    f_nlme_sfo_sfo <- nlme(f_2[\"SFO-SFO\", ])   plot(f_nlme_sfo_sfo)     # With formation fractions this does not coverge with defaults   # f_nlme_sfo_sfo_ff <- nlme(f_2[\"SFO-SFO-ff\", ])   #plot(f_nlme_sfo_sfo_ff)    # For the following, we need to increase pnlsMaxIter and the tolerance   # to get convergence   f_nlme_dfop_sfo <- nlme(f_2[\"DFOP-SFO\", ],     control = list(pnlsMaxIter = 120, tolerance = 5e-4))    plot(f_nlme_dfop_sfo)     anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) #>                 Model df       AIC      BIC    logLik   Test  L.Ratio p-value #> f_nlme_dfop_sfo     1 13  843.8547  884.620 -408.9273                         #> f_nlme_sfo_sfo      2  9 1085.1821 1113.404 -533.5910 1 vs 2 249.3274  <.0001    endpoints(f_nlme_sfo_sfo) #> $ff #> parent_sink   parent_A1     A1_sink  #>   0.5912432   0.4087568   1.0000000  #>  #> $distimes #>            DT50     DT90 #> parent 19.13518  63.5657 #> A1     66.02155 219.3189 #>    endpoints(f_nlme_dfop_sfo) #> $ff #>   parent_A1 parent_sink  #>   0.2768574   0.7231426  #>  #> $distimes #>             DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent  11.07091 104.6320 31.49737 4.462383 46.20825 #> A1     162.30550 539.1672       NA       NA       NA #>     if (length(findFunction(\"varConstProp\")) > 0) { # tc error model for nlme available     # Attempts to fit metabolite kinetics with the tc error model are possible,     # but need tweeking of control values and sometimes do not converge      f_tc <- mmkin(c(\"SFO\", \"DFOP\"), ds, quiet = TRUE, error_model = \"tc\")     f_nlme_sfo_tc <- nlme(f_tc[\"SFO\", ])     f_nlme_dfop_tc <- nlme(f_tc[\"DFOP\", ])     AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)     print(f_nlme_dfop_tc)   } #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -238.4 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)  #> parent_0   log_k1   log_k2 g_qlogis  #> 94.04774 -1.82340 -4.16716  0.05685  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev:    2.474   0.85  1.337   0.4659        1 #>  #> Variance function: #>  Structure: Constant plus proportion of variance covariate #>  Formula: ~fitted(.)  #>  Parameter estimates: #>      const       prop  #> 2.23222933 0.01262399     f_2_obs <- update(f_2, error_model = \"obs\")   f_nlme_sfo_sfo_obs <- nlme(f_2_obs[\"SFO-SFO\", ])   print(f_nlme_sfo_sfo_obs) #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent #> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -473 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1)  #>          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink  #>            87.976            -3.670            -4.164            -4.645  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual #> StdDev:    3.992             1.777           1.055        0.4821    6.483 #>  #> Variance function: #>  Structure: Different standard deviations per stratum #>  Formula: ~1 | name  #>  Parameter estimates: #>    parent        A1  #> 1.0000000 0.2050005    f_nlme_dfop_sfo_obs <- nlme(f_2_obs[\"DFOP-SFO\", ],     control = list(pnlsMaxIter = 120, tolerance = 5e-4))    f_2_tc <- update(f_2, error_model = \"tc\")   # f_nlme_sfo_sfo_tc <- nlme(f_2_tc[\"SFO-SFO\", ]) # No convergence with 50 iterations   # f_nlme_dfop_sfo_tc <- nlme(f_2_tc[\"DFOP-SFO\", ],   #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm    anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs) #>                     Model df      AIC     BIC    logLik   Test  L.Ratio p-value #> f_nlme_dfop_sfo         1 13 843.8547 884.620 -408.9273                         #> f_nlme_dfop_sfo_obs     2 14 817.5338 861.435 -394.7669 1 vs 2 28.32084  <.0001  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"Number observations mkinfit object fitted","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"","code":"# S3 method for class 'mkinfit' nobs(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"object mkinfit object ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"number rows data included mkinfit object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract model parameters — parms","title":"Extract model parameters — parms","text":"function returns degradation model parameters well error model parameters per default, order avoid working fitted model without considering error structure assumed fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract model parameters — parms","text":"","code":"parms(object, ...)  # S3 method for class 'mkinfit' parms(object, transformed = FALSE, errparms = TRUE, ...)  # S3 method for class 'mmkin' parms(object, transformed = FALSE, errparms = TRUE, ...)  # S3 method for class 'multistart' parms(object, exclude_failed = TRUE, ...)  # S3 method for class 'saem.mmkin' parms(object, ci = FALSE, covariates = NULL, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract model parameters — parms","text":"object fitted model object. ... used transformed parameters returned used internally optimisation? errparms error model parameters returned addition degradation parameters? exclude_failed multistart objects, rows failed fits removed returned parameter matrix? ci matrix estimates confidence interval boundaries returned? FALSE (default), vector estimates returned covariates given, otherwise matrix estimates returned, column corresponding row data frame holding covariates covariates data frame holding covariate values return parameter values. effect 'ci' FALSE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract model parameters — parms","text":"Depending object, numeric vector fitted model parameters, matrix (e.g. mmkin row objects), list matrices (e.g. mmkin objects one row).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract model parameters — parms","text":"","code":"# mkinfit objects fit <- mkinfit(\"SFO\", FOCUS_2006_C, quiet = TRUE) parms(fit) #>   parent_0   k_parent      sigma  #> 82.4921598  0.3060633  4.6730124  parms(fit, transformed = TRUE) #>     parent_0 log_k_parent        sigma  #>    82.492160    -1.183963     4.673012   # mmkin objects ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")])) names(ds) <- paste(\"Dataset\", 6:10) # \\dontrun{ fits <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), ds, quiet = TRUE, cores = 1) parms(fits[\"SFO\", ]) #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421 #> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673 parms(fits[, 2]) #> $SFO #>             Dataset 7 #> parent_0 82.666781678 #> k_parent  0.009647805 #> sigma     7.040168584 #>  #> $FOMC #>           Dataset 7 #> parent_0 92.6837649 #> alpha     0.4967832 #> beta     14.1451255 #> sigma     1.9167519 #>  #> $DFOP #>             Dataset 7 #> parent_0 91.058971584 #> k1        0.044946770 #> k2        0.002868336 #> g         0.526942414 #> sigma     2.221302196 #>  parms(fits) #> $SFO #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421 #> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673 #>  #> $FOMC #>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458 #> alpha     1.338667  0.4967832  1.639099  1.074460  0.2805272 #> beta     13.033315 14.1451255  5.007077  4.397126  6.9052224 #> sigma     1.847671  1.9167519  1.066063  3.146056  1.6222778 #>  #> $DFOP #>            Dataset 6    Dataset 7   Dataset 8   Dataset 9   Dataset 10 #> parent_0 96.55213663 91.058971584 90.34509493 98.14858820 94.311323735 #> k1        0.21954588  0.044946770  0.41232288  0.31697588  0.080663857 #> k2        0.02957934  0.002868336  0.07581766  0.03260384  0.003425417 #> g         0.44845068  0.526942414  0.66091967  0.65322767  0.342652880 #> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831 #>  parms(fits, transformed = TRUE) #> $SFO #>              Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0     88.522754 82.666782 86.854731 91.777931  82.148095 #> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737  -4.933090 #> sigma         5.152745  7.040169  3.676964  6.466923   6.504577 #>  #> $FOMC #>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10 #> parent_0  95.5585751 92.6837649 90.7197870 98.38393898  94.848146 #> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085 #> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278 #> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278 #>  #> $DFOP #>           Dataset 6  Dataset 7  Dataset 8  Dataset 9 Dataset 10 #> parent_0 96.5521366 91.0589716 90.3450949 98.1485882 94.3113237 #> log_k1   -1.5161940 -3.1022764 -0.8859486 -1.1489296 -2.5174647 #> log_k2   -3.5206791 -5.8540232 -2.5794240 -3.4233253 -5.6765322 #> g_qlogis -0.2069326  0.1078741  0.6673953  0.6332573 -0.6514943 #> sigma     1.3569047  2.2213022  1.3416908  2.8715985  1.9420678 #>  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot parameter variability of multistart objects — parplot","title":"Plot parameter variability of multistart objects — parplot","text":"Produces boxplot parameters multiple runs, scaled either parameters run highest likelihood, medians proposed paper Duchesne et al. (2021).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot parameter variability of multistart objects — parplot","text":"","code":"parplot(object, ...)  # S3 method for class 'multistart.saem.mmkin' parplot(   object,   llmin = -Inf,   llquant = NA,   scale = c(\"best\", \"median\"),   lpos = \"bottomleft\",   main = \"\",   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot parameter variability of multistart objects — parplot","text":"object multistart object ... Passed boxplot llmin minimum likelihood objects shown llquant Fractional value selecting fits higher likelihoods. Overrides 'llmin'. scale default, scale parameters using best available fit. 'median', parameters scaled using median parameters fits. lpos Positioning legend. main Title plot","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot parameter variability of multistart objects — parplot","text":"Starting values degradation model parameters error model parameters shown green circles. results obtained original run shown red circles.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot parameter variability of multistart objects — parplot","text":"Duchesne R, Guillemin , Gandrillon O, Crauste F. Practical identifiability frame nonlinear mixed effects models: example vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. doi: 10.1186/s12859-021-04373-4.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Plot predictions fitted nonlinear mixed model obtained via mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"","code":"# S3 method for class 'mixed.mmkin' plot(   x,   i = 1:ncol(x$mmkin),   obs_vars = names(x$mkinmod$map),   standardized = TRUE,   covariates = NULL,   covariate_quantiles = c(0.5, 0.05, 0.95),   xlab = \"Time\",   xlim = range(x$data$time),   resplot = c(\"predicted\", \"time\"),   pop_curves = \"auto\",   pred_over = NULL,   test_log_parms = FALSE,   conf.level = 0.6,   default_log_parms = NA,   ymax = \"auto\",   maxabs = \"auto\",   ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),   nrow.legend = ceiling((length(i) + 1)/ncol.legend),   rel.height.legend = 0.02 + 0.07 * nrow.legend,   rel.height.bottom = 1.1,   pch_ds = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],   col_ds = pch_ds + 1,   lty_ds = col_ds,   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"x object class mixed.mmkin, saem.mmkin nlme.mmkin numeric index select datasets plot individual predictions, case plots get large obs_vars character vector names observed variables data model plotted. Defauls observed variables model. standardized residuals standardized? takes effect resplot = \"time\". covariates Data frame covariate values variables covariate models object. given, overrides 'covariate_quantiles'. line data frame result line drawn population. Rownames used legend label lines. covariate_quantiles argument effect fitted object covariate models. , default show three population curves, 5th percentile, 50th percentile 95th percentile covariate values used fitting model. xlab Label x axis. xlim Plot range x direction. resplot residuals plotted time predicted values? pop_curves Per default, one population curve drawn case population parameters fitted model, e.g. saem objects. case covariate model, behaviour depends value 'covariates' pred_over Named list alternative predictions obtained mkinpredict compatible mkinmod. test_log_parms Passed mean_degparms case mixed.mmkin object conf.level Passed mean_degparms case mixed.mmkin object default_log_parms Passed mean_degparms case mixed.mmkin object ymax Vector maximum y axis values maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". ncol.legend Number columns use legend nrow.legend Number rows use legend rel.height.legend relative height legend shown top rel.height.bottom relative height bottom plot row pch_ds Symbols used plotting data. col_ds Colors used plotting observed data corresponding model prediction lines different datasets. lty_ds Line types used model predictions. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Covariate models currently supported saem.mmkin objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"","code":"ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) x$data[c(\"name\", \"time\", \"value\")]) names(ds) <- paste0(\"ds \", 6:10) dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),   A1 = mkinsub(\"SFO\"), quiet = TRUE) # \\dontrun{ f <- mmkin(list(\"DFOP-SFO\" = dfop_sfo), ds, quiet = TRUE) plot(f[, 3:4], standardized = TRUE)   # For this fit we need to increase pnlsMaxiter, and we increase the # tolerance in order to speed up the fit for this example evaluation # It still takes 20 seconds to run f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme)   f_saem <- saem(f, transformations = \"saemix\") plot(f_saem)   f_obs <- mmkin(list(\"DFOP-SFO\" = dfop_sfo), ds, quiet = TRUE, error_model = \"obs\") f_nlmix <- nlmix(f_obs) #> Error in nlmix(f_obs): could not find function \"nlmix\" plot(f_nlmix) #> Error: object 'f_nlmix' not found  # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo,   f_nlme$bparms.optim[-1],   c(parent = f_nlme$bparms.optim[[1]], A1 = 0),   seq(0, 180, by = 0.2)) plot(f_saem, pred_over = list(nlme = pred_nlme))  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"Solves differential equations optimised fixed parameters previous successful call mkinfit plots observed data together solution fitted model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"","code":"# S3 method for class 'mkinfit' plot(   x,   fit = x,   obs_vars = names(fit$mkinmod$map),   xlab = \"Time\",   ylab = \"Residue\",   xlim = range(fit$data$time),   ylim = \"default\",   col_obs = 1:length(obs_vars),   pch_obs = col_obs,   lty_obs = rep(1, length(obs_vars)),   add = FALSE,   legend = !add,   show_residuals = FALSE,   show_errplot = FALSE,   maxabs = \"auto\",   sep_obs = FALSE,   rel.height.middle = 0.9,   row_layout = FALSE,   lpos = \"topright\",   inset = c(0.05, 0.05),   show_errmin = FALSE,   errmin_digits = 3,   frame = TRUE,   ... )  plot_sep(   fit,   show_errmin = TRUE,   show_residuals = ifelse(identical(fit$err_mod, \"const\"), TRUE, \"standardized\"),   ... )  plot_res(   fit,   sep_obs = FALSE,   show_errmin = sep_obs,   standardized = ifelse(identical(fit$err_mod, \"const\"), FALSE, TRUE),   ... )  plot_err(fit, sep_obs = FALSE, show_errmin = sep_obs, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"x Alias fit introduced compatibility generic S3 method. fit object class mkinfit. obs_vars character vector names observed variables data model plotted. Defauls observed variables model. xlab Label x axis. ylab Label y axis. xlim Plot range x direction. ylim Plot range y direction. given list, plot ranges different plot rows can given row layout. col_obs Colors used plotting observed data corresponding model prediction lines. pch_obs Symbols used plotting data. lty_obs Line types used model predictions. add plot added existing plot? legend legend added plot? show_residuals residuals shown? one plot fits shown, residual plot lower third plot. Otherwise, .e. \"sep_obs\" given, residual plots located right plots fitted curves. set 'standardized', plot residuals divided standard deviation given fitted error model shown. show_errplot squared residuals error model shown? one plot fits shown, plot lower third plot.  Otherwise, .e. \"sep_obs\" given, residual plots located right plots fitted curves. maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". sep_obs observed variables shown separate subplots? yes, residual plots requested \"show_residuals\" shown next , plot fits. rel.height.middle relative height middle plot, two rows plots shown. row_layout use row layout residual plot error model plot shown right? lpos Position(s) legend(s). Passed legend first argument.  length one, length obs_var argument. inset Passed legend applicable. show_errmin FOCUS chi2 error value shown upper margin plot? errmin_digits number significant digits rounding FOCUS chi2 error percentage. frame frame drawn around plots? ... arguments passed plot. standardized calling 'plot_res', residuals standardized residual plot?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"current plot device tikz device, latex used formatting chi2 error level, show_errmin = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"","code":"# One parent compound, one metabolite, both single first order, path from # parent to sink included # \\dontrun{ SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"m1\", full = \"Parent\"),                    m1 = mkinsub(\"SFO\", full = \"Metabolite M1\" )) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = \"tc\") #> Warning: Observations with value of zero were removed from the data plot(fit)  plot_res(fit)  plot_res(fit, standardized = FALSE)  plot_err(fit)   # Show the observed variables separately, with residuals plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c(\"topright\", \"bottomright\"),      show_errmin = TRUE)   # The same can be obtained with less typing, using the convenience function plot_sep plot_sep(fit, lpos = c(\"topright\", \"bottomright\"))   # Show the observed variables separately, with the error model plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c(\"topright\", \"bottomright\"),      show_errmin = TRUE)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"x row selected mmkin object ([.mmkin), model fitted least one dataset shown. column, fit least one model dataset shown.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"","code":"# S3 method for class 'mmkin' plot(   x,   main = \"auto\",   legends = 1,   resplot = c(\"time\", \"errmod\"),   ylab = \"Residue\",   standardized = FALSE,   show_errmin = TRUE,   errmin_var = \"All data\",   errmin_digits = 3,   cex = 0.7,   rel.height.middle = 0.9,   ymax = \"auto\",   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"x object class mmkin, either one row one column. main main title placed outer margin plot. legends index fits legends shown. resplot residuals plotted time, using mkinresplot, squared residuals predicted values, error model, using mkinerrplot. ylab Label y axis. standardized residuals standardized? option passed mkinresplot, takes effect resplot = \"time\". show_errmin chi2 error level shown top plots left? errmin_var variable FOCUS chi2 error value shown. errmin_digits number significant digits rounding FOCUS chi2 error percentage. cex Passed plot functions mtext. rel.height.middle relative height middle plot, two rows plots shown. ymax Maximum y axis value plot.mkinfit. ... arguments passed plot.mkinfit mkinresplot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"current plot device tikz device, latex used formatting chi2 error level.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"","code":"# \\dontrun{   # Only use one core not to offend CRAN checks   fits <- mmkin(c(\"FOMC\", \"HS\"),                 list(\"FOCUS B\" = FOCUS_2006_B, \"FOCUS C\" = FOCUS_2006_C), # named list for titles                 cores = 1, quiet = TRUE, error_model = \"tc\") #> Warning: Optimisation did not converge: #> iteration limit reached without convergence (10)   plot(fits[, \"FOCUS C\"])    plot(fits[\"FOMC\", ])    plot(fits[\"FOMC\", ], show_errmin = FALSE)     # We can also plot a single fit, if we like the way plot.mmkin works, but then the plot   # height should be smaller than the plot width (this is not possible for the html pages   # generated by pkgdown, as far as I know).   plot(fits[\"FOMC\", \"FOCUS C\"]) # same as plot(fits[1, 2])     # Show the error models   plot(fits[\"FOMC\", ], resplot = \"errmod\")    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"plots ordered increasing complexity model function (SFO, IORE, DFOP).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"","code":"# S3 method for class 'nafta' plot(x, legend = FALSE, main = \"auto\", ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"x object class nafta. legend legend added? main Possibility override main title plot. ... arguments passed plot.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"Calls plot.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":null,"dir":"Reference","previous_headings":"","what":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"function imports one dataset sheet spreadsheet file. sheets selected based contents sheet 'Datasets', column called 'Dataset Number', containing numbers identifying dataset sheets read . second column must grouping variable, often named 'Soil'. Optionally, time normalization factors can given columns named 'Temperature' 'Moisture'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"","code":"read_spreadsheet(   path,   valid_datasets = \"all\",   parent_only = FALSE,   normalize = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"path Absolute relative path spreadsheet file valid_datasets Optional numeric index valid datasets, default use datasets parent_only parent data used? normalize time scale normalized using temperature moisture normalisation factors sheet 'Datasets'?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"must sheet 'Compounds', columns 'Name' 'Acronym'. first row read header read sheet assumed contain name acronym parent compound. dataset sheets named using dataset numbers read 'Datasets' sheet, .e. '1', '2', ... . dataset sheet, name observed variable (e.g. acronym parent compound one transformation products) first column, time values second colum, observed value third column. case relevant covariate data available, given sheet 'Covariates', containing one line value grouping variable specified 'Datasets'. values first column column must name second column 'Datasets'. Covariates read columns four higher. names preferably contain special characters like spaces, can easily used specifying covariate models. similar data structure defined R6 class mkindsg, probably complicated use.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. lmtest lrtest nlme intervals, nlme","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from an mkinfit model — residuals.mkinfit","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"Extract residuals mkinfit model","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"","code":"# S3 method for class 'mkinfit' residuals(object, standardized = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"object mkinfit object standardized residuals standardized dividing standard deviation obtained fitted error model? ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"","code":"f <- mkinfit(\"DFOP\", FOCUS_2006_C, quiet = TRUE) residuals(f) #> [1]  0.09726374 -0.13912142 -0.15351210  0.73388322 -0.08657004 -0.93204702 #> [7] -0.03269080  1.45347823 -0.88423697 residuals(f, standardized = TRUE) #> [1]  0.13969917 -0.19981904 -0.22048826  1.05407091 -0.12433989 -1.33869208 #> [7] -0.04695355  2.08761977 -1.27002287"},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit nonlinear mixed models with SAEM — saem","title":"Fit nonlinear mixed models with SAEM — saem","text":"function uses saemix::saemix() backend fitting nonlinear mixed effects models created mmkin row objects using Stochastic Approximation Expectation Maximisation algorithm (SAEM).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit nonlinear mixed models with SAEM — saem","text":"","code":"saem(object, ...)  # S3 method for class 'mmkin' saem(   object,   transformations = c(\"mkin\", \"saemix\"),   error_model = \"auto\",   degparms_start = numeric(),   test_log_parms = TRUE,   conf.level = 0.6,   solution_type = \"auto\",   covariance.model = \"auto\",   omega.init = \"auto\",   covariates = NULL,   covariate_models = NULL,   center_covariates = c(NA, \"median\", \"mean\"),   no_random_effect = NULL,   error.init = c(1, 1),   nbiter.saemix = c(300, 100),   control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,     save = FALSE, save.graphs = FALSE),   verbose = FALSE,   quiet = FALSE,   ... )  # S3 method for class 'saem.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)  saemix_model(   object,   solution_type = \"auto\",   transformations = c(\"mkin\", \"saemix\"),   error_model = \"auto\",   degparms_start = numeric(),   covariance.model = \"auto\",   no_random_effect = NULL,   omega.init = \"auto\",   covariates = NULL,   covariate_models = NULL,   error.init = numeric(),   test_log_parms = FALSE,   conf.level = 0.6,   verbose = FALSE,   ... )  saemix_data(object, covariates = NULL, verbose = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit nonlinear mixed models with SAEM — saem","text":"object mmkin row object containing several fits mkinmod model different datasets ... parameters passed saemix::saemixModel. transformations Per default, parameter transformations done mkin. argument set 'saemix', parameter transformations done 'saemix' supported cases, .e. (version 1.1.2) SFO, FOMC, DFOP HS without fixing parent_0, SFO DFOP one SFO metabolite. error_model Possibility override error model used mmkin object degparms_start Parameter values given named numeric vector used override starting values obtained 'mmkin' object. test_log_parms TRUE, attempt made use robust starting values population parameters fitted log parameters mkin (like rate constants) considering rate constants pass t-test calculating mean degradation parameters using mean_degparms. conf.level Possibility adjust required confidence level parameter tested requested 'test_log_parms'. solution_type Possibility specify solution type case automatic choice desired covariance.model passed saemix::saemixModel(). Per default, uncorrelated random effects specified degradation parameters. omega.init passed saemix::saemixModel(). using mkin transformations default covariance model optionally excluded random effects, variances degradation parameters estimated using mean_degparms, testing untransformed log parameters significant difference zero. using mkin transformations custom covariance model, default initialisation saemix::saemixModel used omega.init. covariates data frame covariate data use 'covariate_models', dataset names row names. covariate_models list containing linear model formulas one explanatory variable, .e. type 'parameter ~ covariate'. Covariates must available 'covariates' data frame. center_covariates Either NA, centering, preferred function calculating center, currently either median mean. no_random_effect Character vector degradation parameters variability groups. used covariance model explicitly specified. error.init passed saemix::saemixModel(). nbiter.saemix Convenience option increase number iterations control Passed saemix::saemix. verbose print information created objects type saemix::SaemixModel saemix::SaemixData? quiet suppress messages saemix prints beginning end optimisation process? x saem.mmkin object print digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit nonlinear mixed models with SAEM — saem","text":"S3 object class 'saem.mmkin', containing fitted saemix::SaemixObject list component named ''. object also inherits 'mixed.mmkin'. saemix::SaemixModel object. saemix::SaemixData object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit nonlinear mixed models with SAEM — saem","text":"mmkin row object essentially list mkinfit objects obtained fitting model list datasets using mkinfit. Starting values fixed effects (population mean parameters, argument psi0 saemix::saemixModel() mean values parameters found using mmkin.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit nonlinear mixed models with SAEM — saem","text":"","code":"# \\dontrun{ ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")])) names(ds) <- paste(\"Dataset\", 6:10) f_mmkin_parent_p0_fixed <- mmkin(\"FOMC\", ds,   state.ini = c(parent = 100), fixed_initials = \"parent\", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)  f_mmkin_parent <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent[\"SFO\", ]) f_saem_fomc <- saem(f_mmkin_parent[\"FOMC\", ]) f_saem_dfop <- saem(f_mmkin_parent[\"DFOP\", ]) anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik #> f_saem_sfo     5 624.33 622.38 -307.17 #> f_saem_fomc    7 467.85 465.11 -226.92 #> f_saem_dfop    9 493.76 490.24 -237.88 anova(f_saem_sfo, f_saem_dfop, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     #> f_saem_sfo     5 624.33 622.38 -307.17                          #> f_saem_dfop    9 493.76 490.24 -237.88 138.57  4  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 illparms(f_saem_dfop) #> [1] \"sd(g_qlogis)\" f_saem_dfop_red <- update(f_saem_dfop, no_random_effect = \"g_qlogis\") anova(f_saem_dfop, f_saem_dfop_red, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>                 npar    AIC    BIC     Lik Chisq Df Pr(>Chisq) #> f_saem_dfop_red    8 488.68 485.55 -236.34                     #> f_saem_dfop        9 493.76 490.24 -237.88     0  1          1  anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik #> f_saem_sfo     5 624.33 622.38 -307.17 #> f_saem_fomc    7 467.85 465.11 -226.92 #> f_saem_dfop    9 493.76 490.24 -237.88 # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) #> Loading required package: npde #> Package saemix, version 3.4, March 2024 #>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr #>  #> Attaching package: ‘saemix’ #> The following objects are masked from ‘package:npde’: #>  #>     kurtosis, skewness compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) #> Likelihoods calculated by importance sampling #>        AIC      BIC #> 1 624.3316 622.3788 #> 2 467.8472 465.1132 #> 3 493.7592 490.2441 plot(f_saem_fomc$so, plot.type = \"convergence\")  plot(f_saem_fomc$so, plot.type = \"individual.fit\") #> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde .  plot(f_saem_fomc$so, plot.type = \"npde\") #> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde . #> Please use npdeSaemix to obtain VPC and npde plot(f_saem_fomc$so, plot.type = \"vpc\")   f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = \"tc\") f_saem_fomc_tc <- saem(f_mmkin_parent_tc[\"FOMC\", ]) anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>                npar    AIC    BIC     Lik Chisq Df Pr(>Chisq) #> f_saem_fomc       7 467.85 465.11 -226.92                     #> f_saem_fomc_tc    8 469.90 466.77 -226.95     0  1          1  sfo_sfo <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fomc_sfo <- mkinmod(parent = mkinsub(\"FOMC\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, # and compiled ODEs for FOMC that are much slower f_mmkin <- mmkin(list(     \"SFO-SFO\" = sfo_sfo, \"FOMC-SFO\" = fomc_sfo, \"DFOP-SFO\" = dfop_sfo),   ds, quiet = TRUE) # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds # each on this system, as we use analytical solutions written for saemix. # When using the analytical solutions written for mkin this took around # four minutes f_saem_sfo_sfo <- saem(f_mmkin[\"SFO-SFO\", ]) f_saem_dfop_sfo <- saem(f_mmkin[\"DFOP-SFO\", ]) # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo) #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_A1 * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   839.2 834.1 -406.6 #>  #> Fitted parameters: #>                    estimate    lower   upper #> parent_0           93.70402 91.04104 96.3670 #> log_k_A1           -5.83760 -7.66452 -4.0107 #> f_parent_qlogis    -0.95718 -1.35955 -0.5548 #> log_k1             -2.35514 -3.39402 -1.3163 #> log_k2             -3.79634 -5.64009 -1.9526 #> g_qlogis           -0.02108 -0.66463  0.6225 #> a.1                 1.88191  1.66491  2.0989 #> SD.parent_0         2.81628  0.78922  4.8433 #> SD.log_k_A1         1.78751  0.42105  3.1540 #> SD.f_parent_qlogis  0.45016  0.16116  0.7391 #> SD.log_k1           1.06923  0.31676  1.8217 #> SD.log_k2           2.03768  0.70938  3.3660 #> SD.g_qlogis         0.44024 -0.09262  0.9731 plot(f_saem_dfop_sfo)  summary(f_saem_dfop_sfo, data = TRUE) #> saemix version used for fitting:      3.4  #> mkin version used for pre-fitting:  1.2.11  #> R version used for fitting:         4.5.1  #> Date of fit:     Fri Sep 12 20:34:55 2025  #> Date of summary: Fri Sep 12 20:34:55 2025  #>  #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_A1 * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 4.017 s #> Using 300, 100 iterations and 10 chains #>  #> Variance model: Constant variance  #>  #> Starting values for degradation parameters: #>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2  #>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708  #>        g_qlogis  #>          0.1356  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis #> parent_0           4.941    0.000          0.0000  0.000  0.000   0.0000 #> log_k_A1           0.000    2.551          0.0000  0.000  0.000   0.0000 #> f_parent_qlogis    0.000    0.000          0.7251  0.000  0.000   0.0000 #> log_k1             0.000    0.000          0.0000  1.449  0.000   0.0000 #> log_k2             0.000    0.000          0.0000  0.000  2.228   0.0000 #> g_qlogis           0.000    0.000          0.0000  0.000  0.000   0.7814 #>  #> Starting values for error model parameters: #> a.1  #>   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   839.2 834.1 -406.6 #>  #> Optimised parameters: #>                        est.    lower   upper #> parent_0           93.70402 91.04104 96.3670 #> log_k_A1           -5.83760 -7.66452 -4.0107 #> f_parent_qlogis    -0.95718 -1.35955 -0.5548 #> log_k1             -2.35514 -3.39402 -1.3163 #> log_k2             -3.79634 -5.64009 -1.9526 #> g_qlogis           -0.02108 -0.66463  0.6225 #> a.1                 1.88191  1.66491  2.0989 #> SD.parent_0         2.81628  0.78922  4.8433 #> SD.log_k_A1         1.78751  0.42105  3.1540 #> SD.f_parent_qlogis  0.45016  0.16116  0.7391 #> SD.log_k1           1.06923  0.31676  1.8217 #> SD.log_k2           2.03768  0.70938  3.3660 #> SD.g_qlogis         0.44024 -0.09262  0.9731 #>  #> Correlation:  #>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2  #> log_k_A1        -0.0147                                 #> f_parent_qlogis -0.0269  0.0573                         #> log_k1           0.0263 -0.0011 -0.0040                 #> log_k2           0.0020  0.0065 -0.0002 -0.0776         #> g_qlogis        -0.0248 -0.0180 -0.0004 -0.0903 -0.0603 #>  #> Random effects: #>                      est.    lower  upper #> SD.parent_0        2.8163  0.78922 4.8433 #> SD.log_k_A1        1.7875  0.42105 3.1540 #> SD.f_parent_qlogis 0.4502  0.16116 0.7391 #> SD.log_k1          1.0692  0.31676 1.8217 #> SD.log_k2          2.0377  0.70938 3.3660 #> SD.g_qlogis        0.4402 -0.09262 0.9731 #>  #> Variance model: #>      est. lower upper #> a.1 1.882 1.665 2.099 #>  #> Backtransformed parameters: #>                     est.     lower    upper #> parent_0       93.704015 9.104e+01 96.36699 #> k_A1            0.002916 4.692e-04  0.01812 #> f_parent_to_A1  0.277443 2.043e-01  0.36475 #> k1              0.094880 3.357e-02  0.26813 #> k2              0.022453 3.553e-03  0.14191 #> g               0.494731 3.397e-01  0.65078 #>  #> Resulting formation fractions: #>                 ff #> parent_A1   0.2774 #> parent_sink 0.7226 #>  #> Estimated disappearance times: #>         DT50   DT90 DT50back DT50_k1 DT50_k2 #> parent  14.0  72.38    21.79   7.306   30.87 #> A1     237.7 789.68       NA      NA      NA #>  #> Data: #>          ds   name time observed predicted residual   std standardized #>   Dataset 6 parent    0     97.2  95.70025  1.49975 1.882      0.79693 #>   Dataset 6 parent    0     96.4  95.70025  0.69975 1.882      0.37183 #>   Dataset 6 parent    3     71.1  71.44670 -0.34670 1.882     -0.18423 #>   Dataset 6 parent    3     69.2  71.44670 -2.24670 1.882     -1.19384 #>   Dataset 6 parent    6     58.1  56.59283  1.50717 1.882      0.80087 #>   Dataset 6 parent    6     56.6  56.59283  0.00717 1.882      0.00381 #>   Dataset 6 parent   10     44.4  44.56648 -0.16648 1.882     -0.08847 #>   Dataset 6 parent   10     43.4  44.56648 -1.16648 1.882     -0.61984 #>   Dataset 6 parent   20     33.3  29.76020  3.53980 1.882      1.88096 #>   Dataset 6 parent   20     29.2  29.76020 -0.56020 1.882     -0.29767 #>   Dataset 6 parent   34     17.6  19.39208 -1.79208 1.882     -0.95226 #>   Dataset 6 parent   34     18.0  19.39208 -1.39208 1.882     -0.73971 #>   Dataset 6 parent   55     10.5  10.55761 -0.05761 1.882     -0.03061 #>   Dataset 6 parent   55      9.3  10.55761 -1.25761 1.882     -0.66826 #>   Dataset 6 parent   90      4.5   3.84742  0.65258 1.882      0.34676 #>   Dataset 6 parent   90      4.7   3.84742  0.85258 1.882      0.45304 #>   Dataset 6 parent  112      3.0   2.03997  0.96003 1.882      0.51013 #>   Dataset 6 parent  112      3.4   2.03997  1.36003 1.882      0.72268 #>   Dataset 6 parent  132      2.3   1.14585  1.15415 1.882      0.61328 #>   Dataset 6 parent  132      2.7   1.14585  1.55415 1.882      0.82583 #>   Dataset 6     A1    3      4.3   4.86054 -0.56054 1.882     -0.29786 #>   Dataset 6     A1    3      4.6   4.86054 -0.26054 1.882     -0.13844 #>   Dataset 6     A1    6      7.0   7.74179 -0.74179 1.882     -0.39417 #>   Dataset 6     A1    6      7.2   7.74179 -0.54179 1.882     -0.28789 #>   Dataset 6     A1   10      8.2   9.94048 -1.74048 1.882     -0.92485 #>   Dataset 6     A1   10      8.0   9.94048 -1.94048 1.882     -1.03112 #>   Dataset 6     A1   20     11.0  12.19109 -1.19109 1.882     -0.63291 #>   Dataset 6     A1   20     13.7  12.19109  1.50891 1.882      0.80180 #>   Dataset 6     A1   34     11.5  13.10706 -1.60706 1.882     -0.85395 #>   Dataset 6     A1   34     12.7  13.10706 -0.40706 1.882     -0.21630 #>   Dataset 6     A1   55     14.9  13.06131  1.83869 1.882      0.97703 #>   Dataset 6     A1   55     14.5  13.06131  1.43869 1.882      0.76448 #>   Dataset 6     A1   90     12.1  11.54495  0.55505 1.882      0.29494 #>   Dataset 6     A1   90     12.3  11.54495  0.75505 1.882      0.40122 #>   Dataset 6     A1  112      9.9  10.31533 -0.41533 1.882     -0.22070 #>   Dataset 6     A1  112     10.2  10.31533 -0.11533 1.882     -0.06128 #>   Dataset 6     A1  132      8.8   9.20222 -0.40222 1.882     -0.21373 #>   Dataset 6     A1  132      7.8   9.20222 -1.40222 1.882     -0.74510 #>   Dataset 7 parent    0     93.6  90.82357  2.77643 1.882      1.47532 #>   Dataset 7 parent    0     92.3  90.82357  1.47643 1.882      0.78453 #>   Dataset 7 parent    3     87.0  84.73448  2.26552 1.882      1.20384 #>   Dataset 7 parent    3     82.2  84.73448 -2.53448 1.882     -1.34675 #>   Dataset 7 parent    7     74.0  77.65013 -3.65013 1.882     -1.93958 #>   Dataset 7 parent    7     73.9  77.65013 -3.75013 1.882     -1.99272 #>   Dataset 7 parent   14     64.2  67.60639 -3.40639 1.882     -1.81007 #>   Dataset 7 parent   14     69.5  67.60639  1.89361 1.882      1.00621 #>   Dataset 7 parent   30     54.0  52.53663  1.46337 1.882      0.77760 #>   Dataset 7 parent   30     54.6  52.53663  2.06337 1.882      1.09642 #>   Dataset 7 parent   60     41.1  39.42728  1.67272 1.882      0.88884 #>   Dataset 7 parent   60     38.4  39.42728 -1.02728 1.882     -0.54587 #>   Dataset 7 parent   90     32.5  33.76360 -1.26360 1.882     -0.67144 #>   Dataset 7 parent   90     35.5  33.76360  1.73640 1.882      0.92268 #>   Dataset 7 parent  120     28.1  30.39975 -2.29975 1.882     -1.22203 #>   Dataset 7 parent  120     29.0  30.39975 -1.39975 1.882     -0.74379 #>   Dataset 7 parent  180     26.5  25.62379  0.87621 1.882      0.46559 #>   Dataset 7 parent  180     27.6  25.62379  1.97621 1.882      1.05010 #>   Dataset 7     A1    3      3.9   2.70005  1.19995 1.882      0.63762 #>   Dataset 7     A1    3      3.1   2.70005  0.39995 1.882      0.21252 #>   Dataset 7     A1    7      6.9   5.83475  1.06525 1.882      0.56605 #>   Dataset 7     A1    7      6.6   5.83475  0.76525 1.882      0.40663 #>   Dataset 7     A1   14     10.4  10.26142  0.13858 1.882      0.07364 #>   Dataset 7     A1   14      8.3  10.26142 -1.96142 1.882     -1.04225 #>   Dataset 7     A1   30     14.4  16.82999 -2.42999 1.882     -1.29123 #>   Dataset 7     A1   30     13.7  16.82999 -3.12999 1.882     -1.66319 #>   Dataset 7     A1   60     22.1  22.32486 -0.22486 1.882     -0.11949 #>   Dataset 7     A1   60     22.3  22.32486 -0.02486 1.882     -0.01321 #>   Dataset 7     A1   90     27.5  24.45927  3.04073 1.882      1.61576 #>   Dataset 7     A1   90     25.4  24.45927  0.94073 1.882      0.49988 #>   Dataset 7     A1  120     28.0  25.54862  2.45138 1.882      1.30260 #>   Dataset 7     A1  120     26.6  25.54862  1.05138 1.882      0.55868 #>   Dataset 7     A1  180     25.8  26.82277 -1.02277 1.882     -0.54347 #>   Dataset 7     A1  180     25.3  26.82277 -1.52277 1.882     -0.80916 #>   Dataset 8 parent    0     91.9  91.16791  0.73209 1.882      0.38901 #>   Dataset 8 parent    0     90.8  91.16791 -0.36791 1.882     -0.19550 #>   Dataset 8 parent    1     64.9  67.58358 -2.68358 1.882     -1.42598 #>   Dataset 8 parent    1     66.2  67.58358 -1.38358 1.882     -0.73520 #>   Dataset 8 parent    3     43.5  41.62086  1.87914 1.882      0.99853 #>   Dataset 8 parent    3     44.1  41.62086  2.47914 1.882      1.31735 #>   Dataset 8 parent    8     18.3  19.60116 -1.30116 1.882     -0.69140 #>   Dataset 8 parent    8     18.1  19.60116 -1.50116 1.882     -0.79768 #>   Dataset 8 parent   14     10.2  10.63101 -0.43101 1.882     -0.22903 #>   Dataset 8 parent   14     10.8  10.63101  0.16899 1.882      0.08980 #>   Dataset 8 parent   27      4.9   3.12435  1.77565 1.882      0.94354 #>   Dataset 8 parent   27      3.3   3.12435  0.17565 1.882      0.09334 #>   Dataset 8 parent   48      1.6   0.43578  1.16422 1.882      0.61864 #>   Dataset 8 parent   48      1.5   0.43578  1.06422 1.882      0.56550 #>   Dataset 8 parent   70      1.1   0.05534  1.04466 1.882      0.55510 #>   Dataset 8 parent   70      0.9   0.05534  0.84466 1.882      0.44883 #>   Dataset 8     A1    1      9.6   7.63450  1.96550 1.882      1.04442 #>   Dataset 8     A1    1      7.7   7.63450  0.06550 1.882      0.03481 #>   Dataset 8     A1    3     15.0  15.52593 -0.52593 1.882     -0.27947 #>   Dataset 8     A1    3     15.1  15.52593 -0.42593 1.882     -0.22633 #>   Dataset 8     A1    8     21.2  20.32192  0.87808 1.882      0.46659 #>   Dataset 8     A1    8     21.1  20.32192  0.77808 1.882      0.41345 #>   Dataset 8     A1   14     19.7  20.09721 -0.39721 1.882     -0.21107 #>   Dataset 8     A1   14     18.9  20.09721 -1.19721 1.882     -0.63617 #>   Dataset 8     A1   27     17.5  16.37477  1.12523 1.882      0.59792 #>   Dataset 8     A1   27     15.9  16.37477 -0.47477 1.882     -0.25228 #>   Dataset 8     A1   48      9.5  10.13141 -0.63141 1.882     -0.33551 #>   Dataset 8     A1   48      9.8  10.13141 -0.33141 1.882     -0.17610 #>   Dataset 8     A1   70      6.2   5.81827  0.38173 1.882      0.20284 #>   Dataset 8     A1   70      6.1   5.81827  0.28173 1.882      0.14970 #>   Dataset 9 parent    0     99.8  97.48728  2.31272 1.882      1.22892 #>   Dataset 9 parent    0     98.3  97.48728  0.81272 1.882      0.43186 #>   Dataset 9 parent    1     77.1  79.29476 -2.19476 1.882     -1.16624 #>   Dataset 9 parent    1     77.2  79.29476 -2.09476 1.882     -1.11310 #>   Dataset 9 parent    3     59.0  55.67060  3.32940 1.882      1.76915 #>   Dataset 9 parent    3     58.1  55.67060  2.42940 1.882      1.29092 #>   Dataset 9 parent    8     27.4  31.57871 -4.17871 1.882     -2.22046 #>   Dataset 9 parent    8     29.2  31.57871 -2.37871 1.882     -1.26398 #>   Dataset 9 parent   14     19.1  22.51546 -3.41546 1.882     -1.81489 #>   Dataset 9 parent   14     29.6  22.51546  7.08454 1.882      3.76454 #>   Dataset 9 parent   27     10.1  14.09074 -3.99074 1.882     -2.12057 #>   Dataset 9 parent   27     18.2  14.09074  4.10926 1.882      2.18355 #>   Dataset 9 parent   48      4.5   6.95747 -2.45747 1.882     -1.30584 #>   Dataset 9 parent   48      9.1   6.95747  2.14253 1.882      1.13848 #>   Dataset 9 parent   70      2.3   3.32472 -1.02472 1.882     -0.54451 #>   Dataset 9 parent   70      2.9   3.32472 -0.42472 1.882     -0.22569 #>   Dataset 9 parent   91      2.0   1.64300  0.35700 1.882      0.18970 #>   Dataset 9 parent   91      1.8   1.64300  0.15700 1.882      0.08343 #>   Dataset 9 parent  120      2.0   0.62073  1.37927 1.882      0.73291 #>   Dataset 9 parent  120      2.2   0.62073  1.57927 1.882      0.83918 #>   Dataset 9     A1    1      4.2   3.64568  0.55432 1.882      0.29455 #>   Dataset 9     A1    1      3.9   3.64568  0.25432 1.882      0.13514 #>   Dataset 9     A1    3      7.4   8.30173 -0.90173 1.882     -0.47916 #>   Dataset 9     A1    3      7.9   8.30173 -0.40173 1.882     -0.21347 #>   Dataset 9     A1    8     14.5  12.71589  1.78411 1.882      0.94803 #>   Dataset 9     A1    8     13.7  12.71589  0.98411 1.882      0.52293 #>   Dataset 9     A1   14     14.2  13.90452  0.29548 1.882      0.15701 #>   Dataset 9     A1   14     12.2  13.90452 -1.70452 1.882     -0.90574 #>   Dataset 9     A1   27     13.7  14.15523 -0.45523 1.882     -0.24190 #>   Dataset 9     A1   27     13.2  14.15523 -0.95523 1.882     -0.50759 #>   Dataset 9     A1   48     13.6  13.31038  0.28962 1.882      0.15389 #>   Dataset 9     A1   48     15.4  13.31038  2.08962 1.882      1.11037 #>   Dataset 9     A1   70     10.4  11.85965 -1.45965 1.882     -0.77562 #>   Dataset 9     A1   70     11.6  11.85965 -0.25965 1.882     -0.13797 #>   Dataset 9     A1   91     10.0  10.36294 -0.36294 1.882     -0.19286 #>   Dataset 9     A1   91      9.5  10.36294 -0.86294 1.882     -0.45855 #>   Dataset 9     A1  120      9.1   8.43003  0.66997 1.882      0.35601 #>   Dataset 9     A1  120      9.0   8.43003  0.56997 1.882      0.30287 #>  Dataset 10 parent    0     96.1  93.95603  2.14397 1.882      1.13925 #>  Dataset 10 parent    0     94.3  93.95603  0.34397 1.882      0.18278 #>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237 #>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237 #>  Dataset 10 parent   14     69.4  70.04570 -0.64570 1.882     -0.34311 #>  Dataset 10 parent   14     73.1  70.04570  3.05430 1.882      1.62298 #>  Dataset 10 parent   21     65.6  64.01710  1.58290 1.882      0.84111 #>  Dataset 10 parent   21     65.3  64.01710  1.28290 1.882      0.68170 #>  Dataset 10 parent   41     55.9  54.98434  0.91566 1.882      0.48656 #>  Dataset 10 parent   41     54.4  54.98434 -0.58434 1.882     -0.31050 #>  Dataset 10 parent   63     47.0  49.87137 -2.87137 1.882     -1.52577 #>  Dataset 10 parent   63     49.3  49.87137 -0.57137 1.882     -0.30361 #>  Dataset 10 parent   91     44.7  45.06727 -0.36727 1.882     -0.19516 #>  Dataset 10 parent   91     46.7  45.06727  1.63273 1.882      0.86759 #>  Dataset 10 parent  120     42.1  40.76402  1.33598 1.882      0.70991 #>  Dataset 10 parent  120     41.3  40.76402  0.53598 1.882      0.28481 #>  Dataset 10     A1    8      3.3   4.14599 -0.84599 1.882     -0.44954 #>  Dataset 10     A1    8      3.4   4.14599 -0.74599 1.882     -0.39640 #>  Dataset 10     A1   14      3.9   6.08478 -2.18478 1.882     -1.16093 #>  Dataset 10     A1   14      2.9   6.08478 -3.18478 1.882     -1.69231 #>  Dataset 10     A1   21      6.4   7.59411 -1.19411 1.882     -0.63452 #>  Dataset 10     A1   21      7.2   7.59411 -0.39411 1.882     -0.20942 #>  Dataset 10     A1   41      9.1   9.78292 -0.68292 1.882     -0.36289 #>  Dataset 10     A1   41      8.5   9.78292 -1.28292 1.882     -0.68171 #>  Dataset 10     A1   63     11.7  10.93274  0.76726 1.882      0.40770 #>  Dataset 10     A1   63     12.0  10.93274  1.06726 1.882      0.56711 #>  Dataset 10     A1   91     13.3  11.93986  1.36014 1.882      0.72274 #>  Dataset 10     A1   91     13.2  11.93986  1.26014 1.882      0.66961 #>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111 #>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791  # The following takes about 6 minutes f_saem_dfop_sfo_deSolve <- saem(f_mmkin[\"DFOP-SFO\", ], solution_type = \"deSolve\",   nbiter.saemix = c(200, 80)) #> DINTDY-  T (=R1) illegal       #> In above message, R1 = 70 #>   #>       T not in interval TCUR - HU (= R1) to TCUR (=R2)       #> In above message, R1 = 53.1042, R2 = 56.6326 #>   #> DINTDY-  T (=R1) illegal       #> In above message, R1 = 91 #>   #>       T not in interval TCUR - HU (= R1) to TCUR (=R2)       #> In above message, R1 = 53.1042, R2 = 56.6326 #>   #> DLSODA-  Trouble in DINTDY.  ITASK = I1, TOUT = R1 #> In above message, I1 = 1 #>   #> In above message, R1 = 91 #>   #> Error in deSolve::lsoda(y = odeini, times = outtimes_deSolve, func = lsoda_func,  :  #>   illegal input detected before taking any integration steps - see written message  #anova( #  f_saem_dfop_sfo, #  f_saem_dfop_sfo_deSolve))  # If the model supports it, we can also use eigenvalue based solutions, which # take a similar amount of time #f_saem_sfo_sfo_eigen <- saem(f_mmkin[\"SFO-SFO\", ], solution_type = \"eigen\", #  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"dataset used comparison KinGUI ModelMaker check   software quality KinGUI original publication (Schäfer et al., 2007).   results fitting also included.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"","code":"schaefer07_complex_case"},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"data set data frame 8 observations following 6 variables. time numeric vector parent numeric vector A1 numeric vector B1 numeric vector C1 numeric vector A2 numeric vector results data frame 14 results different parameter values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"Schäfer D, Mikolasch B, Rainbird P Harvey B (2007). KinGUI: new kinetic   software tool evaluations according FOCUS degradation kinetics. : Del   Re AAM, Capri E, Fragoulis G Trevisan M (Eds.). Proceedings XIII   Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"","code":"data <- mkin_wide_to_long(schaefer07_complex_case, time = \"time\") model <- mkinmod(   parent = list(type = \"SFO\", to = c(\"A1\", \"B1\", \"C1\"), sink = FALSE),   A1 = list(type = \"SFO\", to = \"A2\"),   B1 = list(type = \"SFO\"),   C1 = list(type = \"SFO\"),   A2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   # \\dontrun{     fit <- mkinfit(model, data, quiet = TRUE)     plot(fit)      endpoints(fit) #> $ff #>   parent_A1   parent_B1   parent_C1 parent_sink       A1_A2     A1_sink  #>   0.3809618   0.1954668   0.4235714   0.0000000   0.4479540   0.5520460  #>  #> $distimes #>            DT50      DT90 #> parent 13.95078  46.34349 #> A1     49.75347 165.27745 #> B1     37.26905 123.80511 #> C1     11.23129  37.30955 #> A2     28.50690  94.69789 #>    # }  # Compare with the results obtained in the original publication  print(schaefer07_complex_results) #>         compound          parameter  KinGUI ModelMaker deviation #> 1         parent   degradation rate  0.0496     0.0506       2.0 #> 2         parent               DT50 13.9900    13.6900       2.2 #> 3  metabolite A1 formation fraction  0.3803     0.3696       2.9 #> 4  metabolite A1   degradation rate  0.0139     0.0136       2.2 #> 5  metabolite A1               DT50 49.9600    50.8900       1.8 #> 6  metabolite B1 formation fraction  0.1866     0.1818       2.6 #> 7  metabolite B1   degradation rate  0.0175     0.0172       1.7 #> 8  metabolite B1               DT50 39.6100    40.2400       1.6 #> 9  metabolite C1 formation fraction  0.4331     0.4486       3.5 #> 10 metabolite C1   degradation rate  0.0638     0.0700       8.9 #> 11 metabolite C1               DT50 10.8700     9.9000       9.8 #> 12 metabolite A2 formation fraction  0.4529     0.4559       0.7 #> 13 metabolite A2   degradation rate  0.0245     0.0244       0.4 #> 14 metabolite A2               DT50 28.2400    28.4500       0.7"},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":null,"dir":"Reference","previous_headings":"","what":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"function automates replacing unquantified values residue time depth series. time series, function performs part residue processing proposed FOCUS kinetics guidance parent compounds metabolites. two-dimensional residue series time depth, automates proposal Boesten et al (2015).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"","code":"set_nd_nq(res_raw, lod, loq = NA, time_zero_presence = FALSE)  set_nd_nq_focus(   res_raw,   lod,   loq = NA,   set_first_sample_nd = TRUE,   first_sample_nd_value = 0,   ignore_below_loq_after_first_nd = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"res_raw Character vector residue time series, matrix residue values rows representing depth profiles specific sampling time, columns representing time series residues depth. Values limit detection (lod) coded \"nd\", values limit detection limit quantification, , coded \"nq\". Samples analysed coded \"na\". values \"na\", \"nd\" \"nq\" coercible numeric lod Limit detection (numeric) loq Limit quantification(numeric). Must specified FOCUS rule stop first non-detection applied time_zero_presence assume residues occur time zero? affects samples first sampling time reported \"nd\" (detected). set_first_sample_nd first sample set \"first_sample_nd_value\" case non-detection? first_sample_nd_value Value used first sample non-detection ignore_below_loq_after_first_nd ignore values LOQ first non-detection occurs quantified values?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"numeric vector, vector supplied, numeric matrix otherwise","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"set_nd_nq_focus(): Set non-detects residue time series according FOCUS rules","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"Boesten, J. J. T. ., van der Linden, . M. ., Beltman, W. H. J. Pol, J. W. (2015). Leaching plant protection products transformation products; Proposals improving assessment leaching groundwater Netherlands — Version 2. Alterra report 2630, Alterra Wageningen UR (University & Research centre) FOCUS (2014) Generic Guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration, Version 1.1, 18 December 2014, p. 251","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"","code":"# FOCUS (2014) p. 75/76 and 131/132 parent_1 <- c(.12, .09, .05, .03, \"nd\", \"nd\", \"nd\", \"nd\", \"nd\", \"nd\") set_nd_nq(parent_1, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA parent_2 <- c(.12, .09, .05, .03, \"nd\", \"nd\", .03, \"nd\", \"nd\", \"nd\") set_nd_nq(parent_2, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.03 0.01   NA   NA set_nd_nq_focus(parent_2, 0.02, loq = 0.05) #>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA parent_3 <- c(.12, .09, .05, .03, \"nd\", \"nd\", .06, \"nd\", \"nd\", \"nd\") set_nd_nq(parent_3, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA set_nd_nq_focus(parent_3, 0.02, loq = 0.05) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA metabolite <- c(\"nd\", \"nd\", \"nd\", 0.03, 0.06, 0.10, 0.11, 0.10, 0.09, 0.05, 0.03, \"nd\", \"nd\") set_nd_nq(metabolite, 0.02) #>  [1]   NA   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA set_nd_nq_focus(metabolite, 0.02, 0.05) #>  [1] 0.00   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA # # Boesten et al. (2015), p. 57/58 table_8 <- matrix(   c(10, 10, rep(\"nd\", 4),     10, 10, rep(\"nq\", 2), rep(\"nd\", 2),     10, 10, 10, \"nq\", \"nd\", \"nd\",     \"nq\", 10, \"nq\", rep(\"nd\", 3),     \"nd\", \"nq\", \"nq\", rep(\"nd\", 3),     rep(\"nd\", 6), rep(\"nd\", 6)),   ncol = 6, byrow = TRUE) set_nd_nq(table_8, 0.5, 1.5, time_zero_presence = TRUE) #>       [,1]  [,2]  [,3] [,4] [,5] [,6] #> [1,] 10.00 10.00  0.25 0.25   NA   NA #> [2,] 10.00 10.00  1.00 1.00 0.25   NA #> [3,] 10.00 10.00 10.00 1.00 0.25   NA #> [4,]  1.00 10.00  1.00 0.25   NA   NA #> [5,]  0.25  1.00  1.00 0.25   NA   NA #> [6,]    NA  0.25  0.25   NA   NA   NA #> [7,]    NA    NA    NA   NA   NA   NA table_10 <- matrix(   c(10, 10, rep(\"nd\", 4),     10, 10, rep(\"nd\", 4),     10, 10, 10, rep(\"nd\", 3),     \"nd\", 10, rep(\"nd\", 4),     rep(\"nd\", 18)),   ncol = 6, byrow = TRUE) set_nd_nq(table_10, 0.5, time_zero_presence = TRUE) #>       [,1]  [,2]  [,3] [,4] [,5] [,6] #> [1,] 10.00 10.00  0.25   NA   NA   NA #> [2,] 10.00 10.00  0.25   NA   NA   NA #> [3,] 10.00 10.00 10.00 0.25   NA   NA #> [4,]  0.25 10.00  0.25   NA   NA   NA #> [5,]    NA  0.25    NA   NA   NA   NA #> [6,]    NA    NA    NA   NA   NA   NA #> [7,]    NA    NA    NA   NA   NA   NA"},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Two-component error model — sigma_twocomp","title":"Two-component error model — sigma_twocomp","text":"Function describing standard deviation measurement error dependence measured value \\(y\\):","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Two-component error model — sigma_twocomp","text":"","code":"sigma_twocomp(y, sigma_low, rsd_high)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Two-component error model — sigma_twocomp","text":"y magnitude observed value sigma_low asymptotic minimum standard deviation low observed values rsd_high coefficient describing increase standard deviation magnitude observed value","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Two-component error model — sigma_twocomp","text":"standard deviation response variable.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Two-component error model — sigma_twocomp","text":"$$\\sigma = \\sqrt{ \\sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2) error model used example Werner et al. (1978). model proposed Rocke Lorenzato (1995) can written form well, assumes approximate lognormal distribution errors high values y.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Two-component error model — sigma_twocomp","text":"Werner, Mario, Brooks, Samuel H., Knott, Lancaster B. (1978) Additive, Multiplicative, Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898. Rocke, David M. Lorenzato, Stefan (1995) two-component model measurement error analytical chemistry. Technometrics 37(2), 176-184. Ranke J Meinecke S (2019) Error Models Kinetic Evaluation Chemical Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124 .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Two-component error model — sigma_twocomp","text":"","code":"times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120) d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times)) set.seed(123456) d_syn <- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),   reps = 2, n = 1)[[1]] f_nls <- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,  start = list(parent_0 = 100, lrc = -3)) library(nlme) f_gnls <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),   data = d_syn, na.action = na.omit,   start = list(parent_0 = 100, lrc = -3)) if (length(findFunction(\"varConstProp\")) > 0) {   f_gnls_tc <- update(f_gnls, weights = varConstProp())   f_gnls_tc_sf <- update(f_gnls_tc, control = list(sigma = 1)) } f_mkin <- mkinfit(\"SFO\", d_syn, error_model = \"const\", quiet = TRUE) f_mkin_tc <- mkinfit(\"SFO\", d_syn, error_model = \"tc\", quiet = TRUE) plot_res(f_mkin_tc, standardized = TRUE)  AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc) #>              df      AIC #> f_nls         3 114.4817 #> f_gnls        3 114.4817 #> f_gnls_tc     5 103.6447 #> f_gnls_tc_sf  4 101.6447 #> f_mkin        3 114.4817 #> f_mkin_tc     4 101.6446"},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get status information for fit array objects — status","title":"Method to get status information for fit array objects — status","text":"Method get status information fit array objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get status information for fit array objects — status","text":"","code":"status(object, ...)  # S3 method for class 'mmkin' status(object, ...)  # S3 method for class 'status.mmkin' print(x, ...)  # S3 method for class 'mhmkin' status(object, ...)  # S3 method for class 'status.mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get status information for fit array objects — status","text":"object object investigate ... potential future extensions x object printed","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get status information for fit array objects — status","text":"object dimensions fit array suitable printing method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Method to get status information for fit array objects — status","text":"","code":"# \\dontrun{ fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS B\" = FOCUS_2006_C),   quiet = TRUE) status(fits) #>       dataset #> model  FOCUS A FOCUS B #>   SFO  OK      OK      #>   FOMC C       OK      #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters uncertainty statistics, chi2 error levels calculated according FOCUS guidance (2006) defined therein, formation fractions, DT50 values optionally data, consisting observed, predicted residual values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'mkinfit' summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)  # S3 method for class 'summary.mkinfit' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class mkinfit. data logical, indicating whether data included summary. distimes logical, indicating whether DT50 DT90 values included. alpha error level confidence interval estimation t distribution ... optional arguments passed methods like print. x object class summary.mkinfit. digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list components, among others version, Rversion mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used model use_of_ff maximum minimum use made formation fractions bpar Optimised backtransformed parameters data data (see Description ). start starting values bounds, applicable, optimised parameters. fixed values fixed parameters. errmin chi2 error levels observed variable. bparms.ode backtransformed ODE parameters, use starting parameters related models. errparms Error model parameters. ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues fractional eigenvector component g SFORB systems model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary method for class ","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"summary(mkinfit(\"SFO\", FOCUS_2006_A, quiet = TRUE)) #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:26:43 2025  #> Date of summary: Mon May 12 20:26:43 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #>  #> Model predictions using solution type analytical  #>  #> Fitted using 131 model solutions performed in 0.01 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>           value   type #> parent_0 101.24  state #> k_parent   0.10 deparm #>  #> Starting values for the transformed parameters actually optimised: #>                   value lower upper #> parent_0     101.240000  -Inf   Inf #> log_k_parent  -2.302585  -Inf   Inf #>  #> Fixed parameter values: #> None #>  #> Results: #>  #>        AIC     BIC    logLik #>   55.28197 55.5203 -24.64099 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>              Estimate Std. Error  Lower   Upper #> parent_0      109.200    3.70400 99.630 118.700 #> log_k_parent   -3.291    0.09176 -3.527  -3.055 #> sigma           5.266    1.31600  1.882   8.649 #>  #> Parameter correlation: #>               parent_0 log_k_parent     sigma #> parent_0     1.000e+00    5.428e-01 1.642e-07 #> log_k_parent 5.428e-01    1.000e+00 2.507e-07 #> sigma        1.642e-07    2.507e-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_0 109.20000   29.47 4.218e-07 99.6300 118.70000 #> k_parent   0.03722   10.90 5.650e-05  0.0294   0.04712 #> sigma      5.26600    4.00 5.162e-03  1.8820   8.64900 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   8.385       2  6 #> parent     8.385       2  6 #>  #> Estimated disappearance times: #>         DT50  DT90 #> parent 18.62 61.87 #>  #> Data: #>  time variable observed predicted residual #>     0   parent   101.24   109.153  -7.9132 #>     3   parent    99.27    97.622   1.6484 #>     7   parent    90.11    84.119   5.9913 #>    14   parent    72.19    64.826   7.3641 #>    30   parent    29.71    35.738  -6.0283 #>    62   parent     5.98    10.862  -4.8818 #>    90   parent     1.54     3.831  -2.2911 #>   118   parent     0.39     1.351  -0.9613"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Shows status information mkinfit objects contained object gives overview ill-defined parameters calculated illparms.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'mmkin' summary(object, conf.level = 0.95, ...)  # S3 method for class 'summary.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class mmkin conf.level confidence level testing parameters ... optional arguments passed methods like print. x object class summary.mmkin. digits number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS C\" = FOCUS_2006_C),   quiet = TRUE, cores = 1) #> Warning: Optimisation did not converge: #> false convergence (8)   summary(fits) #> Error model: Constant variance  #> Fitted in 0.533 s #>  #> Status: #>       dataset #> model  FOCUS A FOCUS C #>   SFO  OK      OK      #>   FOMC C       OK      #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings #>  #> Ill-defined parameters: #>       dataset #> model  FOCUS A                      FOCUS C #>   SFO                                       #>   FOMC parent_0, alpha, beta, sigma"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters fixed effects (population), random effects (deviations population mean) residual error model, well resulting endpoints formation fractions DT50 values. Optionally (default FALSE), data listed full.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'nlme.mmkin' summary(   object,   data = FALSE,   verbose = FALSE,   distimes = TRUE,   alpha = 0.05,   ... )  # S3 method for class 'summary.nlme.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), verbose = x$verbose, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class nlme.mmkin data logical, indicating whether full data included summary. verbose summary verbose? distimes logical, indicating whether DT50 DT90 values included. alpha error level confidence interval estimation t distribution ... optional arguments passed methods like print. x object class summary.nlme.mmkin digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list based nlme object obtained fit, least following additional components nlmeversion, mkinversion, Rversion nlme, mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used degradation model use_of_ff maximum minimum use made formation fractions data data confint_trans Transformed parameters used optimisation, confidence intervals confint_back Backtransformed parameters, confidence intervals available ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues SFORB components model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke mkin specific parts José Pinheiro Douglas Bates components inherited nlme","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"# Generate five datasets following SFO kinetics sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) dt50_sfo_in_pop <- 50 k_in_pop <- log(2) / dt50_sfo_in_pop set.seed(1234) k_in <- rlnorm(5, log(k_in_pop), 0.5) SFO <- mkinmod(parent = mkinsub(\"SFO\"))  pred_sfo <- function(k) {   mkinpredict(SFO,     c(k_parent = k),     c(parent = 100),     sampling_times) }  ds_sfo_mean <- lapply(k_in, pred_sfo) names(ds_sfo_mean) <- paste(\"ds\", 1:5)  set.seed(12345) ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),     n = 1)[[1]] })  # \\dontrun{ # Evaluate using mmkin and nlme library(nlme) f_mmkin <- mmkin(\"SFO\", ds_sfo_syn, quiet = TRUE, error_model = \"tc\", cores = 1) #> Warning: Optimisation did not converge: #> iteration limit reached without convergence (10) f_nlme <- nlme(f_mmkin) summary(f_nlme, data = TRUE) #> nlme version used for fitting:      3.1.168  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.0  #> Date of fit:     Mon May 12 20:26:45 2025  #> Date of summary: Mon May 12 20:26:45 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 0.193 s using 4 iterations #>  #> Variance model: Two-component variance function  #>  #> Mean of starting values for individual parameters: #>     parent_0 log_k_parent  #>      101.569       -4.454  #>  #> Fixed degradation parameter values: #> None #>  #> Results: #>  #>     AIC   BIC logLik #>   584.5 599.5 -286.2 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>               lower    est.   upper #> parent_0     99.371 101.592 103.814 #> log_k_parent -4.973  -4.449  -3.926 #>  #> Correlation:  #>              parnt_0 #> log_k_parent 0.0507  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1) #>  Level: ds #>  Structure: Diagonal #>          parent_0 log_k_parent Residual #> StdDev: 6.921e-05       0.5863        1 #>  #> Variance function: #>  Structure: Constant plus proportion of variance covariate #>  Formula: ~fitted(.)  #>  Parameter estimates: #>        const         prop  #> 0.0001208313 0.0789967985  #>  #> Backtransformed parameters with asymmetric confidence intervals: #>              lower      est.     upper #> parent_0 99.370882 101.59243 103.81398 #> k_parent  0.006923   0.01168   0.01972 #>  #> Estimated disappearance times: #>         DT50  DT90 #> parent 59.32 197.1 #>  #> Data: #>    ds   name time observed predicted  residual    std standardized #>  ds 1 parent    0    104.1   101.592   2.50757 8.0255     0.312451 #>  ds 1 parent    0    105.0   101.592   3.40757 8.0255     0.424594 #>  ds 1 parent    1     98.5   100.796  -2.29571 7.9625    -0.288313 #>  ds 1 parent    1     96.1   100.796  -4.69571 7.9625    -0.589725 #>  ds 1 parent    3    101.9    99.221   2.67904 7.8381     0.341796 #>  ds 1 parent    3     85.2    99.221 -14.02096 7.8381    -1.788812 #>  ds 1 parent    7     99.1    96.145   2.95512 7.5951     0.389081 #>  ds 1 parent    7     93.0    96.145  -3.14488 7.5951    -0.414065 #>  ds 1 parent   14     88.1    90.989  -2.88944 7.1879    -0.401987 #>  ds 1 parent   14     84.1    90.989  -6.88944 7.1879    -0.958480 #>  ds 1 parent   28     80.2    81.493  -1.29305 6.4377    -0.200857 #>  ds 1 parent   28     91.3    81.493   9.80695 6.4377     1.523364 #>  ds 1 parent   60     65.1    63.344   1.75642 5.0039     0.351008 #>  ds 1 parent   60     65.8    63.344   2.45642 5.0039     0.490898 #>  ds 1 parent   90     47.8    50.018  -2.21764 3.9512    -0.561252 #>  ds 1 parent   90     53.5    50.018   3.48236 3.9512     0.881335 #>  ds 1 parent  120     37.6    39.495  -1.89515 3.1200    -0.607423 #>  ds 1 parent  120     39.3    39.495  -0.19515 3.1200    -0.062549 #>  ds 2 parent    0    107.9   101.592   6.30757 8.0255     0.785943 #>  ds 2 parent    0    102.1   101.592   0.50757 8.0255     0.063245 #>  ds 2 parent    1    103.8   100.058   3.74159 7.9043     0.473361 #>  ds 2 parent    1    108.6   100.058   8.54159 7.9043     1.080626 #>  ds 2 parent    3     91.0    97.060  -6.05952 7.6674    -0.790297 #>  ds 2 parent    3     84.9    97.060 -12.15952 7.6674    -1.585874 #>  ds 2 parent    7     79.3    91.329 -12.02867 7.2147    -1.667251 #>  ds 2 parent    7    100.9    91.329   9.57133 7.2147     1.326647 #>  ds 2 parent   14     77.3    82.102  -4.80185 6.4858    -0.740366 #>  ds 2 parent   14     83.5    82.102   1.39815 6.4858     0.215571 #>  ds 2 parent   28     66.8    66.351   0.44945 5.2415     0.085748 #>  ds 2 parent   28     63.3    66.351  -3.05055 5.2415    -0.582002 #>  ds 2 parent   60     40.8    40.775   0.02474 3.2211     0.007679 #>  ds 2 parent   60     44.8    40.775   4.02474 3.2211     1.249485 #>  ds 2 parent   90     27.8    25.832   1.96762 2.0407     0.964198 #>  ds 2 parent   90     27.0    25.832   1.16762 2.0407     0.572171 #>  ds 2 parent  120     15.2    16.366  -1.16561 1.2928    -0.901595 #>  ds 2 parent  120     15.5    16.366  -0.86561 1.2928    -0.669547 #>  ds 3 parent    0     97.7   101.592  -3.89243 8.0255    -0.485009 #>  ds 3 parent    0     88.2   101.592 -13.39243 8.0255    -1.668740 #>  ds 3 parent    1    109.9    99.218  10.68196 7.8379     1.362858 #>  ds 3 parent    1     97.8    99.218  -1.41804 7.8379    -0.180921 #>  ds 3 parent    3    100.5    94.634   5.86555 7.4758     0.784603 #>  ds 3 parent    3     77.4    94.634 -17.23445 7.4758    -2.305360 #>  ds 3 parent    7     78.3    86.093  -7.79273 6.8011    -1.145813 #>  ds 3 parent    7     90.3    86.093   4.20727 6.8011     0.618620 #>  ds 3 parent   14     76.0    72.958   3.04222 5.7634     0.527848 #>  ds 3 parent   14     79.1    72.958   6.14222 5.7634     1.065722 #>  ds 3 parent   28     46.0    52.394  -6.39404 4.1390    -1.544842 #>  ds 3 parent   28     53.4    52.394   1.00596 4.1390     0.243046 #>  ds 3 parent   60     25.1    24.582   0.51786 1.9419     0.266676 #>  ds 3 parent   60     21.4    24.582  -3.18214 1.9419    -1.638664 #>  ds 3 parent   90     11.0    12.092  -1.09202 0.9552    -1.143199 #>  ds 3 parent   90     14.2    12.092   2.10798 0.9552     2.206777 #>  ds 3 parent  120      5.8     5.948  -0.14810 0.4699    -0.315178 #>  ds 3 parent  120      6.1     5.948   0.15190 0.4699     0.323282 #>  ds 4 parent    0     95.3   101.592  -6.29243 8.0255    -0.784057 #>  ds 4 parent    0    102.0   101.592   0.40757 8.0255     0.050784 #>  ds 4 parent    1    104.4   101.125   3.27549 7.9885     0.410025 #>  ds 4 parent    1    105.4   101.125   4.27549 7.9885     0.535205 #>  ds 4 parent    3    113.7   100.195  13.50487 7.9151     1.706218 #>  ds 4 parent    3     82.3   100.195 -17.89513 7.9151    -2.260886 #>  ds 4 parent    7     98.1    98.362  -0.26190 7.7703    -0.033706 #>  ds 4 parent    7     87.8    98.362 -10.56190 7.7703    -1.359270 #>  ds 4 parent   14     97.9    95.234   2.66590 7.5232     0.354357 #>  ds 4 parent   14    104.8    95.234   9.56590 7.5232     1.271521 #>  ds 4 parent   28     85.0    89.274  -4.27372 7.0523    -0.606001 #>  ds 4 parent   28     77.2    89.274 -12.07372 7.0523    -1.712017 #>  ds 4 parent   60     82.2    77.013   5.18661 6.0838     0.852526 #>  ds 4 parent   60     86.1    77.013   9.08661 6.0838     1.493571 #>  ds 4 parent   90     70.5    67.053   3.44692 5.2970     0.650733 #>  ds 4 parent   90     61.7    67.053  -5.35308 5.2970    -1.010591 #>  ds 4 parent  120     60.0    58.381   1.61905 4.6119     0.351058 #>  ds 4 parent  120     56.4    58.381  -1.98095 4.6119    -0.429530 #>  ds 5 parent    0     92.6   101.592  -8.99243 8.0255    -1.120485 #>  ds 5 parent    0    116.5   101.592  14.90757 8.0255     1.857531 #>  ds 5 parent    1    108.0    99.914   8.08560 7.8929     1.024413 #>  ds 5 parent    1    104.9    99.914   4.98560 7.8929     0.631655 #>  ds 5 parent    3    100.5    96.641   3.85898 7.6343     0.505477 #>  ds 5 parent    3     89.5    96.641  -7.14102 7.6343    -0.935383 #>  ds 5 parent    7     91.7    90.412   1.28752 7.1423     0.180267 #>  ds 5 parent    7     95.1    90.412   4.68752 7.1423     0.656304 #>  ds 5 parent   14     82.2    80.463   1.73715 6.3563     0.273295 #>  ds 5 parent   14     84.5    80.463   4.03715 6.3563     0.635141 #>  ds 5 parent   28     60.5    63.728  -3.22788 5.0343    -0.641178 #>  ds 5 parent   28     72.8    63.728   9.07212 5.0343     1.802062 #>  ds 5 parent   60     38.3    37.399   0.90061 2.9544     0.304835 #>  ds 5 parent   60     40.7    37.399   3.30061 2.9544     1.117174 #>  ds 5 parent   90     22.5    22.692  -0.19165 1.7926    -0.106913 #>  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273 #>  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259 #>  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters fixed effects (population), random effects (deviations population mean) residual error model, well resulting endpoints formation fractions DT50 values. Optionally (default FALSE), data listed full.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'saem.mmkin' summary(   object,   data = FALSE,   verbose = FALSE,   covariates = NULL,   covariate_quantile = 0.5,   distimes = TRUE,   ... )  # S3 method for class 'summary.saem.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), verbose = x$verbose, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class saem.mmkin data logical, indicating whether full data included summary. verbose summary verbose? covariates Numeric vector covariate values variables covariate models object. given, overrides 'covariate_quantile'. covariate_quantile argument effect fitted object covariate models. , default show endpoints median covariate values (50th percentile). distimes logical, indicating whether DT50 DT90 values included. ... optional arguments passed methods like print. x object class summary.saem.mmkin digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list based saemix::SaemixObject obtained fit, least following additional components saemixversion, mkinversion, Rversion saemix, mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used degradation model use_of_ff maximum minimum use made formation fractions data data confint_trans Transformed parameters used optimisation, confidence intervals confint_back Backtransformed parameters, confidence intervals available confint_errmod Error model parameters confidence intervals ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues SFORB components model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke mkin specific parts saemix authors parts inherited saemix.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"# Generate five datasets following DFOP-SFO kinetics sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"m1\"),  m1 = mkinsub(\"SFO\"), quiet = TRUE) set.seed(1234) k1_in <- rlnorm(5, log(0.1), 0.3) k2_in <- rlnorm(5, log(0.02), 0.3) g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) k_m1_in <- rlnorm(5, log(0.02), 0.3)  pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {   mkinpredict(dfop_sfo,     c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),     c(parent = 100, m1 = 0),     sampling_times) }  ds_mean_dfop_sfo <- lapply(1:5, function(i) {   mkinpredict(dfop_sfo,     c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],       f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),     c(parent = 100, m1 = 0),     sampling_times) }) names(ds_mean_dfop_sfo) <- paste(\"ds\", 1:5)  ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),     n = 1)[[1]] })  # \\dontrun{ # Evaluate using mmkin and saem f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,   quiet = TRUE, error_model = \"tc\", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) print(f_saem_dfop_sfo) #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 171 observations of 2 variable(s) grouped in 5 datasets #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   810.8 805.4 -391.4 #>  #> Fitted parameters: #>                      estimate     lower     upper #> parent_0           100.966822  97.90584 104.02780 #> log_k_m1            -4.076164  -4.17485  -3.97748 #> f_parent_qlogis     -0.940902  -1.35358  -0.52823 #> log_k1              -2.363988  -2.71690  -2.01107 #> log_k2              -4.060016  -4.21743  -3.90260 #> g_qlogis            -0.029999  -0.44766   0.38766 #> a.1                  0.876272   0.67790   1.07464 #> b.1                  0.079594   0.06521   0.09398 #> SD.parent_0          0.076322 -76.45825  76.61089 #> SD.log_k_m1          0.005052  -1.08943   1.09953 #> SD.f_parent_qlogis   0.446968   0.16577   0.72816 #> SD.log_k1            0.348786   0.09502   0.60255 #> SD.log_k2            0.147456   0.03111   0.26380 #> SD.g_qlogis          0.348244   0.02794   0.66854 illparms(f_saem_dfop_sfo) #> [1] \"sd(parent_0)\" \"sd(log_k_m1)\" f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo,   no_random_effect = c(\"parent_0\", \"log_k_m1\")) illparms(f_saem_dfop_sfo_2) intervals(f_saem_dfop_sfo_2) #> Approximate 95% confidence intervals #>  #>  Fixed effects: #>                      lower         est.        upper #> parent_0       98.04247057 101.09950884 104.15654711 #> k_m1            0.01528983   0.01687734   0.01862969 #> f_parent_to_m1  0.20447650   0.27932896   0.36887691 #> k1              0.06779844   0.09638524   0.13702550 #> k2              0.01495629   0.01741775   0.02028431 #> g               0.37669311   0.48368409   0.59219202 #>  #>  Random effects: #>                          lower      est.     upper #> sd(f_parent_qlogis) 0.16515113 0.4448330 0.7245148 #> sd(log_k1)          0.08982399 0.3447403 0.5996565 #> sd(log_k2)          0.02806780 0.1419560 0.2558443 #> sd(g_qlogis)        0.04908644 0.3801993 0.7113121 #>  #>   #>          lower       est.      upper #> a.1 0.67993373 0.87630147 1.07266921 #> b.1 0.06522297 0.07920531 0.09318766 summary(f_saem_dfop_sfo_2, data = TRUE) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.0  #> Date of fit:     Mon May 12 20:27:02 2025  #> Date of summary: Mon May 12 20:27:02 2025  #>  #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 171 observations of 2 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 9.206 s #> Using 300, 100 iterations and 10 chains #>  #> Variance model: Two-component variance function  #>  #> Starting values for degradation parameters: #>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2  #>       101.65645        -4.05368        -0.94311        -2.35943        -4.07006  #>        g_qlogis  #>        -0.01133  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>                 parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis #> parent_0           6.742   0.0000          0.0000 0.0000 0.0000    0.000 #> log_k_m1           0.000   0.2236          0.0000 0.0000 0.0000    0.000 #> f_parent_qlogis    0.000   0.0000          0.5572 0.0000 0.0000    0.000 #> log_k1             0.000   0.0000          0.0000 0.8031 0.0000    0.000 #> log_k2             0.000   0.0000          0.0000 0.0000 0.2931    0.000 #> g_qlogis           0.000   0.0000          0.0000 0.0000 0.0000    0.807 #>  #> Starting values for error model parameters: #> a.1 b.1  #>   1   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   806.9 802.2 -391.5 #>  #> Optimised parameters: #>                         est.    lower     upper #> parent_0           101.09951 98.04247 104.15655 #> log_k_m1            -4.08178 -4.18057  -3.98300 #> f_parent_qlogis     -0.94779 -1.35855  -0.53704 #> log_k1              -2.33940 -2.69122  -1.98759 #> log_k2              -4.05027 -4.20262  -3.89791 #> g_qlogis            -0.06529 -0.50361   0.37303 #> a.1                  0.87630  0.67993   1.07267 #> b.1                  0.07921  0.06522   0.09319 #> SD.f_parent_qlogis   0.44483  0.16515   0.72451 #> SD.log_k1            0.34474  0.08982   0.59966 #> SD.log_k2            0.14196  0.02807   0.25584 #> SD.g_qlogis          0.38020  0.04909   0.71131 #>  #> Correlation:  #>                 parnt_0 lg_k_m1 f_prnt_ log_k1  log_k2  #> log_k_m1        -0.4716                                 #> f_parent_qlogis -0.2394  0.2617                         #> log_k1           0.1677 -0.1566 -0.0659                 #> log_k2           0.0165  0.0638  0.0045  0.2013         #> g_qlogis         0.1118 -0.1118 -0.0340 -0.2324 -0.3419 #>  #> Random effects: #>                      est.   lower  upper #> SD.f_parent_qlogis 0.4448 0.16515 0.7245 #> SD.log_k1          0.3447 0.08982 0.5997 #> SD.log_k2          0.1420 0.02807 0.2558 #> SD.g_qlogis        0.3802 0.04909 0.7113 #>  #> Variance model: #>        est.   lower   upper #> a.1 0.87630 0.67993 1.07267 #> b.1 0.07921 0.06522 0.09319 #>  #> Backtransformed parameters: #>                     est.    lower     upper #> parent_0       101.09951 98.04247 104.15655 #> k_m1             0.01688  0.01529   0.01863 #> f_parent_to_m1   0.27933  0.20448   0.36888 #> k1               0.09639  0.06780   0.13703 #> k2               0.01742  0.01496   0.02028 #> g                0.48368  0.37669   0.59219 #>  #> Resulting formation fractions: #>                 ff #> parent_m1   0.2793 #> parent_sink 0.7207 #>  #> Estimated disappearance times: #>         DT50   DT90 DT50back DT50_k1 DT50_k2 #> parent 15.66  94.28    28.38   7.191    39.8 #> m1     41.07 136.43       NA      NA      NA #>  #> Data: #>    ds   name time observed  predicted  residual    std standardized #>  ds 1 parent    0     89.8  1.011e+02 -11.29951 8.0554    -1.402721 #>  ds 1 parent    0    104.1  1.011e+02   3.00049 8.0554     0.372481 #>  ds 1 parent    1     88.7  9.624e+01  -7.53600 7.6726    -0.982195 #>  ds 1 parent    1     95.5  9.624e+01  -0.73600 7.6726    -0.095925 #>  ds 1 parent    3     81.8  8.736e+01  -5.55672 6.9744    -0.796732 #>  ds 1 parent    3     94.5  8.736e+01   7.14328 6.9744     1.024217 #>  ds 1 parent    7     71.5  7.251e+01  -1.00511 5.8093    -0.173019 #>  ds 1 parent    7     70.3  7.251e+01  -2.20511 5.8093    -0.379585 #>  ds 1 parent   14     54.2  5.356e+01   0.63921 4.3319     0.147560 #>  ds 1 parent   14     49.6  5.356e+01  -3.96079 4.3319    -0.914340 #>  ds 1 parent   28     31.5  3.175e+01  -0.25429 2.6634    -0.095475 #>  ds 1 parent   28     28.8  3.175e+01  -2.95429 2.6634    -1.109218 #>  ds 1 parent   60     12.1  1.281e+01  -0.71388 1.3409    -0.532390 #>  ds 1 parent   60     13.6  1.281e+01   0.78612 1.3409     0.586271 #>  ds 1 parent   90      6.2  6.405e+00  -0.20462 1.0125    -0.202083 #>  ds 1 parent   90      8.3  6.405e+00   1.89538 1.0125     1.871910 #>  ds 1 parent  120      2.2  3.329e+00  -1.12941 0.9151    -1.234165 #>  ds 1 parent  120      2.4  3.329e+00  -0.92941 0.9151    -1.015615 #>  ds 1     m1    1      0.3  1.177e+00  -0.87699 0.8812    -0.995168 #>  ds 1     m1    1      0.2  1.177e+00  -0.97699 0.8812    -1.108644 #>  ds 1     m1    3      2.2  3.268e+00  -1.06821 0.9137    -1.169063 #>  ds 1     m1    3      3.0  3.268e+00  -0.26821 0.9137    -0.293536 #>  ds 1     m1    7      6.5  6.555e+00  -0.05539 1.0186    -0.054377 #>  ds 1     m1    7      5.0  6.555e+00  -1.55539 1.0186    -1.527022 #>  ds 1     m1   14     10.2  1.017e+01   0.03108 1.1902     0.026117 #>  ds 1     m1   14      9.5  1.017e+01  -0.66892 1.1902    -0.562010 #>  ds 1     m1   28     12.2  1.270e+01  -0.50262 1.3342    -0.376708 #>  ds 1     m1   28     13.4  1.270e+01   0.69738 1.3342     0.522686 #>  ds 1     m1   60     11.8  1.078e+01   1.01734 1.2236     0.831403 #>  ds 1     m1   60     13.2  1.078e+01   2.41734 1.2236     1.975530 #>  ds 1     m1   90      6.6  7.686e+00  -1.08586 1.0670    -1.017675 #>  ds 1     m1   90      9.3  7.686e+00   1.61414 1.0670     1.512779 #>  ds 1     m1  120      3.5  5.205e+00  -1.70467 0.9684    -1.760250 #>  ds 1     m1  120      5.4  5.205e+00   0.19533 0.9684     0.201701 #>  ds 2 parent    0    118.0  1.011e+02  16.90049 8.0554     2.098026 #>  ds 2 parent    0     99.8  1.011e+02  -1.29951 8.0554    -0.161321 #>  ds 2 parent    1     90.2  9.574e+01  -5.53784 7.6334    -0.725473 #>  ds 2 parent    1     94.6  9.574e+01  -1.13784 7.6334    -0.149060 #>  ds 2 parent    3     96.1  8.638e+01   9.72233 6.8975     1.409551 #>  ds 2 parent    3     78.4  8.638e+01  -7.97767 6.8975    -1.156610 #>  ds 2 parent    7     77.9  7.194e+01   5.95854 5.7651     1.033547 #>  ds 2 parent    7     77.7  7.194e+01   5.75854 5.7651     0.998856 #>  ds 2 parent   14     56.0  5.558e+01   0.42141 4.4885     0.093888 #>  ds 2 parent   14     54.7  5.558e+01  -0.87859 4.4885    -0.195742 #>  ds 2 parent   28     36.6  3.852e+01  -1.92382 3.1746    -0.605999 #>  ds 2 parent   28     36.8  3.852e+01  -1.72382 3.1746    -0.543000 #>  ds 2 parent   60     22.1  2.108e+01   1.02043 1.8856     0.541168 #>  ds 2 parent   60     24.7  2.108e+01   3.62043 1.8856     1.920034 #>  ds 2 parent   90     12.4  1.250e+01  -0.09675 1.3220    -0.073184 #>  ds 2 parent   90     10.8  1.250e+01  -1.69675 1.3220    -1.283492 #>  ds 2 parent  120      6.8  7.426e+00  -0.62587 1.0554    -0.593027 #>  ds 2 parent  120      7.9  7.426e+00   0.47413 1.0554     0.449242 #>  ds 2     m1    1      1.3  1.417e+00  -0.11735 0.8835    -0.132825 #>  ds 2     m1    3      3.7  3.823e+00  -0.12301 0.9271    -0.132673 #>  ds 2     m1    3      4.7  3.823e+00   0.87699 0.9271     0.945909 #>  ds 2     m1    7      8.1  7.288e+00   0.81180 1.0494     0.773619 #>  ds 2     m1    7      7.9  7.288e+00   0.61180 1.0494     0.583025 #>  ds 2     m1   14     10.1  1.057e+01  -0.46957 1.2119    -0.387459 #>  ds 2     m1   14     10.3  1.057e+01  -0.26957 1.2119    -0.222432 #>  ds 2     m1   28     10.7  1.234e+01  -1.63555 1.3124    -1.246185 #>  ds 2     m1   28     12.2  1.234e+01  -0.13555 1.3124    -0.103281 #>  ds 2     m1   60     10.7  1.065e+01   0.04641 1.2165     0.038151 #>  ds 2     m1   60     12.5  1.065e+01   1.84641 1.2165     1.517773 #>  ds 2     m1   90      9.1  8.177e+00   0.92337 1.0896     0.847403 #>  ds 2     m1   90      7.4  8.177e+00  -0.77663 1.0896    -0.712734 #>  ds 2     m1  120      6.1  5.966e+00   0.13404 0.9956     0.134631 #>  ds 2     m1  120      4.5  5.966e+00  -1.46596 0.9956    -1.472460 #>  ds 3 parent    0    106.2  1.011e+02   5.10049 8.0554     0.633175 #>  ds 3 parent    0    106.9  1.011e+02   5.80049 8.0554     0.720073 #>  ds 3 parent    1    107.4  9.365e+01  13.74627 7.4695     1.840332 #>  ds 3 parent    1     96.1  9.365e+01   2.44627 7.4695     0.327504 #>  ds 3 parent    3     79.4  8.139e+01  -1.99118 6.5059    -0.306058 #>  ds 3 parent    3     82.6  8.139e+01   1.20882 6.5059     0.185803 #>  ds 3 parent    7     63.9  6.445e+01  -0.54666 5.1792    -0.105549 #>  ds 3 parent    7     62.4  6.445e+01  -2.04666 5.1792    -0.395170 #>  ds 3 parent   14     51.0  4.830e+01   2.69944 3.9247     0.687800 #>  ds 3 parent   14     47.1  4.830e+01  -1.20056 3.9247    -0.305896 #>  ds 3 parent   28     36.1  3.426e+01   1.83885 2.8516     0.644839 #>  ds 3 parent   28     36.6  3.426e+01   2.33885 2.8516     0.820177 #>  ds 3 parent   60     20.1  1.968e+01   0.42208 1.7881     0.236053 #>  ds 3 parent   60     19.8  1.968e+01   0.12208 1.7881     0.068273 #>  ds 3 parent   90     11.3  1.194e+01  -0.64013 1.2893    -0.496496 #>  ds 3 parent   90     10.7  1.194e+01  -1.24013 1.2893    -0.961865 #>  ds 3 parent  120      8.2  7.247e+00   0.95264 1.0476     0.909381 #>  ds 3 parent  120      7.3  7.247e+00   0.05264 1.0476     0.050254 #>  ds 3     m1    0      0.8 -2.956e-12   0.80000 0.8763     0.912928 #>  ds 3     m1    1      1.8  1.757e+00   0.04318 0.8873     0.048666 #>  ds 3     m1    1      2.3  1.757e+00   0.54318 0.8873     0.612186 #>  ds 3     m1    3      4.2  4.566e+00  -0.36607 0.9480    -0.386149 #>  ds 3     m1    3      4.1  4.566e+00  -0.46607 0.9480    -0.491634 #>  ds 3     m1    7      6.8  8.157e+00  -1.35680 1.0887    -1.246241 #>  ds 3     m1    7     10.1  8.157e+00   1.94320 1.0887     1.784855 #>  ds 3     m1   14     11.4  1.085e+01   0.55367 1.2272     0.451182 #>  ds 3     m1   14     12.8  1.085e+01   1.95367 1.2272     1.592023 #>  ds 3     m1   28     11.5  1.149e+01   0.01098 1.2633     0.008689 #>  ds 3     m1   28     10.6  1.149e+01  -0.88902 1.2633    -0.703717 #>  ds 3     m1   60      7.5  9.295e+00  -1.79500 1.1445    -1.568351 #>  ds 3     m1   60      8.6  9.295e+00  -0.69500 1.1445    -0.607245 #>  ds 3     m1   90      7.3  7.017e+00   0.28305 1.0377     0.272775 #>  ds 3     m1   90      8.1  7.017e+00   1.08305 1.0377     1.043720 #>  ds 3     m1  120      5.3  5.087e+00   0.21272 0.9645     0.220547 #>  ds 3     m1  120      3.8  5.087e+00  -1.28728 0.9645    -1.334660 #>  ds 4 parent    0    104.7  1.011e+02   3.60049 8.0554     0.446965 #>  ds 4 parent    0     88.3  1.011e+02 -12.79951 8.0554    -1.588930 #>  ds 4 parent    1     94.2  9.755e+01  -3.35176 7.7762    -0.431030 #>  ds 4 parent    1     94.6  9.755e+01  -2.95176 7.7762    -0.379591 #>  ds 4 parent    3     78.1  9.095e+01 -12.85198 7.2570    -1.770981 #>  ds 4 parent    3     96.5  9.095e+01   5.54802 7.2570     0.764508 #>  ds 4 parent    7     76.2  7.949e+01  -3.29267 6.3569    -0.517966 #>  ds 4 parent    7     77.8  7.949e+01  -1.69267 6.3569    -0.266272 #>  ds 4 parent   14     70.8  6.384e+01   6.95621 5.1321     1.355423 #>  ds 4 parent   14     67.3  6.384e+01   3.45621 5.1321     0.673445 #>  ds 4 parent   28     43.1  4.345e+01  -0.35291 3.5515    -0.099370 #>  ds 4 parent   28     45.1  4.345e+01   1.64709 3.5515     0.463771 #>  ds 4 parent   60     21.3  2.137e+01  -0.07478 1.9063    -0.039229 #>  ds 4 parent   60     23.5  2.137e+01   2.12522 1.9063     1.114813 #>  ds 4 parent   90     11.8  1.205e+01  -0.24925 1.2957    -0.192375 #>  ds 4 parent   90     12.1  1.205e+01   0.05075 1.2957     0.039168 #>  ds 4 parent  120      7.0  6.967e+00   0.03315 1.0356     0.032013 #>  ds 4 parent  120      6.2  6.967e+00  -0.76685 1.0356    -0.740510 #>  ds 4     m1    0      1.6  1.421e-13   1.60000 0.8763     1.825856 #>  ds 4     m1    1      0.9  7.250e-01   0.17503 0.8782     0.199310 #>  ds 4     m1    3      3.7  2.038e+00   1.66201 0.8910     1.865236 #>  ds 4     m1    3      2.0  2.038e+00  -0.03799 0.8910    -0.042637 #>  ds 4     m1    7      3.6  4.186e+00  -0.58623 0.9369    -0.625692 #>  ds 4     m1    7      3.8  4.186e+00  -0.38623 0.9369    -0.412230 #>  ds 4     m1   14      7.1  6.752e+00   0.34768 1.0266     0.338666 #>  ds 4     m1   14      6.6  6.752e+00  -0.15232 1.0266    -0.148372 #>  ds 4     m1   28      9.5  9.034e+00   0.46628 1.1313     0.412159 #>  ds 4     m1   28      9.3  9.034e+00   0.26628 1.1313     0.235373 #>  ds 4     m1   60      8.3  8.634e+00  -0.33359 1.1115    -0.300112 #>  ds 4     m1   60      9.0  8.634e+00   0.36641 1.1115     0.329645 #>  ds 4     m1   90      6.6  6.671e+00  -0.07091 1.0233    -0.069295 #>  ds 4     m1   90      7.7  6.671e+00   1.02909 1.0233     1.005691 #>  ds 4     m1  120      3.7  4.823e+00  -1.12301 0.9559    -1.174763 #>  ds 4     m1  120      3.5  4.823e+00  -1.32301 0.9559    -1.383979 #>  ds 5 parent    0    110.4  1.011e+02   9.30049 8.0554     1.154563 #>  ds 5 parent    0    112.1  1.011e+02  11.00049 8.0554     1.365601 #>  ds 5 parent    1     93.5  9.440e+01  -0.90098 7.5282    -0.119681 #>  ds 5 parent    1     91.0  9.440e+01  -3.40098 7.5282    -0.451764 #>  ds 5 parent    3     71.0  8.287e+01 -11.86698 6.6217    -1.792122 #>  ds 5 parent    3     89.7  8.287e+01   6.83302 6.6217     1.031907 #>  ds 5 parent    7     60.4  6.562e+01  -5.22329 5.2711    -0.990936 #>  ds 5 parent    7     59.1  6.562e+01  -6.52329 5.2711    -1.237566 #>  ds 5 parent   14     56.5  4.739e+01   9.10588 3.8548     2.362225 #>  ds 5 parent   14     47.0  4.739e+01  -0.39412 3.8548    -0.102240 #>  ds 5 parent   28     30.2  3.118e+01  -0.98128 2.6206    -0.374451 #>  ds 5 parent   28     23.9  3.118e+01  -7.28128 2.6206    -2.778500 #>  ds 5 parent   60     17.0  1.804e+01  -1.03959 1.6761    -0.620224 #>  ds 5 parent   60     18.7  1.804e+01   0.66041 1.6761     0.394008 #>  ds 5 parent   90     11.3  1.165e+01  -0.35248 1.2727    -0.276958 #>  ds 5 parent   90     11.9  1.165e+01   0.24752 1.2727     0.194488 #>  ds 5 parent  120      9.0  7.556e+00   1.44368 1.0612     1.360449 #>  ds 5 parent  120      8.1  7.556e+00   0.54368 1.0612     0.512338 #>  ds 5     m1    0      0.7 -1.421e-14   0.70000 0.8763     0.798812 #>  ds 5     m1    1      3.0  3.160e+00  -0.15979 0.9113    -0.175340 #>  ds 5     m1    1      2.6  3.160e+00  -0.55979 0.9113    -0.614254 #>  ds 5     m1    3      5.1  8.448e+00  -3.34789 1.1026    -3.036487 #>  ds 5     m1    3      7.5  8.448e+00  -0.94789 1.1026    -0.859720 #>  ds 5     m1    7     16.5  1.581e+01   0.68760 1.5286     0.449839 #>  ds 5     m1    7     19.0  1.581e+01   3.18760 1.5286     2.085373 #>  ds 5     m1   14     22.9  2.218e+01   0.71983 1.9632     0.366658 #>  ds 5     m1   14     23.2  2.218e+01   1.01983 1.9632     0.519469 #>  ds 5     m1   28     22.2  2.425e+01  -2.05105 2.1113    -0.971479 #>  ds 5     m1   28     24.4  2.425e+01   0.14895 2.1113     0.070552 #>  ds 5     m1   60     15.5  1.876e+01  -3.25968 1.7250    -1.889646 #>  ds 5     m1   60     19.8  1.876e+01   1.04032 1.7250     0.603074 #>  ds 5     m1   90     14.9  1.365e+01   1.25477 1.3914     0.901806 #>  ds 5     m1   90     14.2  1.365e+01   0.55477 1.3914     0.398714 #>  ds 5     m1  120     10.9  9.726e+00   1.17443 1.1667     1.006587 #>  ds 5     m1  120     10.4  9.726e+00   0.67443 1.1667     0.578044 # Add a correlation between random effects of g and k2 cov_model_3 <- f_saem_dfop_sfo_2$so@model@covariance.model cov_model_3[\"log_k2\", \"g_qlogis\"] <- 1 cov_model_3[\"g_qlogis\", \"log_k2\"] <- 1 f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo,   covariance.model = cov_model_3) intervals(f_saem_dfop_sfo_3) #> Approximate 95% confidence intervals #>  #>  Fixed effects: #>                      lower         est.        upper #> parent_0       98.42519529 101.51623115 104.60726702 #> k_m1            0.01505059   0.01662123   0.01835577 #> f_parent_to_m1  0.20100222   0.27477835   0.36332008 #> k1              0.07347479   0.10139028   0.13991179 #> k2              0.01469861   0.01771120   0.02134125 #> g               0.35506898   0.46263682   0.57379888 #>  #>  Random effects: #>                             lower       est.     upper #> sd(f_parent_qlogis)    0.16472883  0.4435866 0.7224443 #> sd(log_k1)             0.05323856  0.2981783 0.5431180 #> sd(log_k2)             0.05013379  0.1912531 0.3323723 #> sd(g_qlogis)           0.04710647  0.3997298 0.7523531 #> corr(log_k2,g_qlogis) -1.31087397 -0.5845703 0.1417334 #>  #>   #>          lower       est.      upper #> a.1 0.67769608 0.87421677 1.07073746 #> b.1 0.06525119 0.07925135 0.09325151 # The correlation does not improve the fit judged by AIC and BIC, although # the likelihood is higher with the additional parameter anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3) #> Data: 171 observations of 2 variable(s) grouped in 5 datasets #>  #>                   npar    AIC    BIC     Lik #> f_saem_dfop_sfo_2   12 806.91 802.23 -391.46 #> f_saem_dfop_sfo_3   13 807.96 802.88 -390.98 #> f_saem_dfop_sfo     14 810.83 805.36 -391.41 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":null,"dir":"Reference","previous_headings":"","what":"Display the output of a summary function according to the output format — summary_listing","title":"Display the output of a summary function according to the output format — summary_listing","text":"function intended use R markdown code chunk chunk option results = \"asis\".","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display the output of a summary function according to the output format — summary_listing","text":"","code":"summary_listing(object, caption = NULL, label = NULL, clearpage = TRUE)  tex_listing(object, caption = NULL, label = NULL, clearpage = TRUE)  html_listing(object, caption = NULL)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display the output of a summary function according to the output format — summary_listing","text":"object object summary listed caption optional caption label optional label, ignored html output clearpage new page started listing? Ignored html output","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"12 datasets generated using four different models three different  variance components. four models either SFO DFOP model either  two sequential two parallel metabolites. Variance component '' based normal distribution standard deviation 3,  Variance component 'b' also based normal distribution, standard deviation 7.  Variance component 'c' based error model Rocke Lorenzato (1995),  minimum standard deviation (small y values) 0.5, proportionality constant 0.07  increase standard deviation y. Note simplified version  error model proposed Rocke Lorenzato (1995), model error  measured values approximates lognormal distribution high values, whereas using  normally distributed error components along. Initial concentrations metabolites values adding variance component resulted  value assumed limit detection 0.1 set NA. example, first dataset title SFO_lin_a based SFO model  two sequential metabolites (linear pathway), added variance component ''. Compare also code example section see degradation models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"","code":"synthetic_data_for_UBA_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"list containing twelve datasets R6 class defined mkinds,   containing, among others, following components title name dataset, e.g. SFO_lin_a data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452 Rocke, David M. und Lorenzato, Stefan (1995) two-component model   measurement error analytical chemistry. Technometrics 37(2), 176-184.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"","code":"# \\dontrun{ # The data have been generated using the following kinetic models m_synth_SFO_lin <- mkinmod(parent = list(type = \"SFO\", to = \"M1\"),                            M1 = list(type = \"SFO\", to = \"M2\"),                            M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   m_synth_SFO_par <- mkinmod(parent = list(type = \"SFO\", to = c(\"M1\", \"M2\"),                                          sink = FALSE),                            M1 = list(type = \"SFO\"),                            M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_DFOP_lin <- mkinmod(parent = list(type = \"DFOP\", to = \"M1\"),                             M1 = list(type = \"SFO\", to = \"M2\"),                             M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_DFOP_par <- mkinmod(parent = list(type = \"DFOP\", to = c(\"M1\", \"M2\"),                                           sink = FALSE),                             M1 = list(type = \"SFO\"),                             M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  # The model predictions without intentional error were generated as follows sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)  d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,                                c(k_parent = 0.7, f_parent_to_M1 = 0.8,                                  k_M1 = 0.3, f_M1_to_M2 = 0.7,                                  k_M2 = 0.02),                                c(parent = 100, M1 = 0, M2 = 0),                                sampling_times)  d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,                                 c(k1 = 0.2, k2 = 0.02, g = 0.5,                                   f_parent_to_M1 = 0.5, k_M1 = 0.3,                                   f_M1_to_M2 = 0.7, k_M2 = 0.02),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,                                c(k_parent = 0.2,                                  f_parent_to_M1 = 0.8, k_M1 = 0.01,                                  f_parent_to_M2 = 0.2, k_M2 = 0.02),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,                                c(k1 = 0.3, k2 = 0.02, g = 0.7,                                  f_parent_to_M1 = 0.6, k_M1 = 0.04,                                  f_parent_to_M2 = 0.4, k_M2 = 0.01),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  # Construct names for datasets with errors d_synth_names = paste0(\"d_synth_\", c(\"SFO_lin\", \"SFO_par\",                                      \"DFOP_lin\", \"DFOP_par\"))  # Original function used or adding errors. The add_err function now published # with this package is a slightly generalised version where the names of # secondary compartments that should have an initial value of zero (M1 and M2 # in this case) are not hardcoded any more. # add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789) # { #   set.seed(seed) #   d_long = mkin_wide_to_long(d, time = \"time\") #   d_rep = data.frame(lapply(d_long, rep, each = 2)) #   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value)) # #   d_rep[d_rep$time == 0 & d_rep$name %in% c(\"M1\", \"M2\"), \"value\"] <- 0 #   d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value)) #   d_NA$value <- round(d_NA$value, 1) #   return(d_NA) # }  # The following is the simplified version of the two-component model of Rocke # and Lorenzato (1995) sdfunc_twocomp = function(value, sd_low, rsd_high) {   sqrt(sd_low^2 + value^2 * rsd_high^2) }  # Add the errors. for (d_synth_name in d_synth_names) {   d_synth = get(d_synth_name)   assign(paste0(d_synth_name, \"_a\"), add_err(d_synth, function(value) 3))   assign(paste0(d_synth_name, \"_b\"), add_err(d_synth, function(value) 7))   assign(paste0(d_synth_name, \"_c\"), add_err(d_synth,                            function(value) sdfunc_twocomp(value, 0.5, 0.07)))  }  d_synth_err_names = c(   paste(rep(d_synth_names, each = 3), letters[1:3], sep = \"_\") )  # This is just one example of an evaluation using the kinetic model used for # the generation of the data   fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,                  quiet = TRUE)   plot_sep(fit)    summary(fit) #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:27:12 2025  #> Date of summary: Mon May 12 20:27:12 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1 #> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2 #>  #> Model predictions using solution type deSolve  #>  #> Fitted using 848 model solutions performed in 0.176 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>                   value   type #> parent_0       101.3500  state #> k_parent         0.1000 deparm #> k_M1             0.1001 deparm #> k_M2             0.1002 deparm #> f_parent_to_M1   0.5000 deparm #> f_M1_to_M2       0.5000 deparm #>  #> Starting values for the transformed parameters actually optimised: #>                      value lower upper #> parent_0        101.350000  -Inf   Inf #> log_k_parent     -2.302585  -Inf   Inf #> log_k_M1         -2.301586  -Inf   Inf #> log_k_M2         -2.300587  -Inf   Inf #> f_parent_qlogis   0.000000  -Inf   Inf #> f_M1_qlogis       0.000000  -Inf   Inf #>  #> Fixed parameter values: #>      value  type #> M1_0     0 state #> M2_0     0 state #>  #> Results: #>  #>        AIC      BIC    logLik #>   188.7274 200.3723 -87.36368 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>                 Estimate Std. Error   Lower    Upper #> parent_0        102.1000    1.57000 98.8600 105.3000 #> log_k_parent     -0.3020    0.03885 -0.3812  -0.2229 #> log_k_M1         -1.2070    0.07123 -1.3520  -1.0620 #> log_k_M2         -3.9010    0.06571 -4.0350  -3.7670 #> f_parent_qlogis   1.2010    0.23530  0.7216   1.6800 #> f_M1_qlogis       0.9589    0.24890  0.4520   1.4660 #> sigma             2.2730    0.25740  1.7490   2.7970 #>  #> Parameter correlation: #>                   parent_0 log_k_parent   log_k_M1   log_k_M2 f_parent_qlogis #> parent_0         1.000e+00    3.933e-01 -1.605e-01  2.819e-02      -4.624e-01 #> log_k_parent     3.933e-01    1.000e+00 -4.082e-01  7.166e-02      -5.682e-01 #> log_k_M1        -1.605e-01   -4.082e-01  1.000e+00 -3.929e-01       7.478e-01 #> log_k_M2         2.819e-02    7.166e-02 -3.929e-01  1.000e+00      -2.658e-01 #> f_parent_qlogis -4.624e-01   -5.682e-01  7.478e-01 -2.658e-01       1.000e+00 #> f_M1_qlogis      1.614e-01    4.102e-01 -8.109e-01  5.419e-01      -8.605e-01 #> sigma           -1.377e-08    7.536e-10  1.089e-08 -4.422e-08       7.124e-09 #>                 f_M1_qlogis      sigma #> parent_0          1.614e-01 -1.377e-08 #> log_k_parent      4.102e-01  7.536e-10 #> log_k_M1         -8.109e-01  1.089e-08 #> log_k_M2          5.419e-01 -4.422e-08 #> f_parent_qlogis  -8.605e-01  7.124e-09 #> f_M1_qlogis       1.000e+00 -2.685e-08 #> sigma            -2.685e-08  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       102.10000  65.000 7.281e-36 98.86000 105.30000 #> k_parent         0.73930  25.740 2.948e-23  0.68310   0.80020 #> k_M1             0.29920  14.040 1.577e-15  0.25880   0.34590 #> k_M2             0.02023  15.220 1.653e-16  0.01769   0.02312 #> f_parent_to_M1   0.76870  18.370 7.295e-19  0.67300   0.84290 #> f_M1_to_M2       0.72290  14.500 6.418e-16  0.61110   0.81240 #> sigma            2.27300   8.832 2.161e-10  1.74900   2.79700 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   8.454       6 17 #> parent     8.660       2  6 #> M1        10.583       2  5 #> M2         3.586       2  6 #>  #> Resulting formation fractions: #>                 ff #> parent_M1   0.7687 #> parent_sink 0.2313 #> M1_M2       0.7229 #> M1_sink     0.2771 #>  #> Estimated disappearance times: #>           DT50    DT90 #> parent  0.9376   3.114 #> M1      2.3170   7.697 #> M2     34.2689 113.839 #>  #> Data: #>  time variable observed  predicted residual #>     0   parent    101.5  1.021e+02 -0.56248 #>     0   parent    101.2  1.021e+02 -0.86248 #>     1   parent     53.9  4.873e+01  5.17118 #>     1   parent     47.5  4.873e+01 -1.22882 #>     3   parent     10.4  1.111e+01 -0.70773 #>     3   parent      7.6  1.111e+01 -3.50773 #>     7   parent      1.1  5.772e-01  0.52283 #>     7   parent      0.3  5.772e-01 -0.27717 #>    14   parent      3.5  3.264e-03  3.49674 #>    28   parent      3.2  1.045e-07  3.20000 #>    90   parent      0.6  9.532e-10  0.60000 #>   120   parent      3.5 -5.940e-10  3.50000 #>     1       M1     36.4  3.479e+01  1.61088 #>     1       M1     37.4  3.479e+01  2.61088 #>     3       M1     34.3  3.937e+01 -5.07027 #>     3       M1     39.8  3.937e+01  0.42973 #>     7       M1     15.1  1.549e+01 -0.38715 #>     7       M1     17.8  1.549e+01  2.31285 #>    14       M1      5.8  1.995e+00  3.80469 #>    14       M1      1.2  1.995e+00 -0.79531 #>    60       M1      0.5  2.111e-06  0.50000 #>    90       M1      3.2 -9.672e-10  3.20000 #>   120       M1      1.5  7.670e-10  1.50000 #>   120       M1      0.6  7.670e-10  0.60000 #>     1       M2      4.8  4.455e+00  0.34517 #>     3       M2     20.9  2.153e+01 -0.62527 #>     3       M2     19.3  2.153e+01 -2.22527 #>     7       M2     42.0  4.192e+01  0.07941 #>     7       M2     43.1  4.192e+01  1.17941 #>    14       M2     49.4  4.557e+01  3.83353 #>    14       M2     44.3  4.557e+01 -1.26647 #>    28       M2     34.6  3.547e+01 -0.87275 #>    28       M2     33.0  3.547e+01 -2.47275 #>    60       M2     18.8  1.858e+01  0.21837 #>    60       M2     17.6  1.858e+01 -0.98163 #>    90       M2     10.6  1.013e+01  0.47130 #>    90       M2     10.8  1.013e+01  0.67130 #>   120       M2      9.8  5.521e+00  4.27893 #>   120       M2      3.3  5.521e+00 -2.22107 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"datasets used comparative validation several kinetic evaluation  software packages (Ranke, 2014).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"","code":"test_data_from_UBA_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"list containing three datasets R6 class defined mkinds.   dataset , among others, following components title name dataset, e.g. UBA_2014_WS_river data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"","code":"# \\dontrun{   # This is a level P-II evaluation of the dataset according to the FOCUS kinetics   # guidance. Due to the strong correlation of the parameter estimates, the   # covariance matrix is not returned. Note that level P-II evaluations are   # generally considered deprecated due to the frequent occurrence of such   # large parameter correlations, among other reasons (e.g. the adequacy of the   # model).   m_ws <- mkinmod(parent_w = mkinsub(\"SFO\", \"parent_s\"),                   parent_s = mkinsub(\"SFO\", \"parent_w\")) #> Temporary DLL for differentials generated and loaded   f_river <- mkinfit(m_ws, test_data_from_UBA_2014[[1]]$data, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data   plot_sep(f_river)     summary(f_river)$bpar #>                           Estimate se_notrans    t value       Pr(>t) #> parent_w_0             95.98567441 2.16285684 44.3791159 1.245593e-17 #> k_parent_w              0.42068803 0.05573864  7.5475120 8.752928e-07 #> k_parent_s              0.07419672 0.10108562  0.7339987 2.371337e-01 #> f_parent_w_to_parent_s  0.14336920 0.05809346  2.4679062 1.305295e-02 #> f_parent_s_to_parent_w  1.00000000 3.13868615  0.3186046 3.772097e-01 #> sigma                   2.98287858 0.68923267  4.3278253 2.987160e-04 #>                              Lower       Upper #> parent_w_0             91.48420501 100.4871438 #> k_parent_w              0.36593946   0.4836276 #> k_parent_s              0.02289956   0.2404043 #> f_parent_w_to_parent_s  0.08180934   0.2391848 #> f_parent_s_to_parent_w  0.00000000   1.0000000 #> sigma                   2.00184022   3.9639169   mkinerrmin(f_river) #>             err.min n.optim df #> All data 0.09246946       5  6 #> parent_w 0.06377096       3  3 #> parent_s 0.20882325       2  3    # This is the evaluation used for the validation of software packages   # in the expertise from 2014   m_soil <- mkinmod(parent = mkinsub(\"SFO\", c(\"M1\", \"M2\")),                     M1 = mkinsub(\"SFO\", \"M3\"),                     M2 = mkinsub(\"SFO\", \"M3\"),                     M3 = mkinsub(\"SFO\"),                     use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded    f_soil <- mkinfit(m_soil, test_data_from_UBA_2014[[3]]$data, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data   plot_sep(f_soil, lpos = c(\"topright\", \"topright\", \"topright\", \"bottomright\"))    summary(f_soil)$bpar #>                   Estimate  se_notrans    t value       Pr(>t)        Lower #> parent_0       76.55425650 0.859186398 89.1008711 1.113861e-26 74.755959420 #> k_parent        0.12081956 0.004601918 26.2541722 1.077359e-16  0.111561575 #> k_M1            0.84258615 0.806159719  1.0451851 1.545266e-01  0.113779564 #> k_M2            0.04210880 0.017083034  2.4649483 1.170188e-02  0.018013857 #> k_M3            0.01122918 0.007245855  1.5497385 6.885051e-02  0.002909431 #> f_parent_to_M1  0.32240200 0.240783878  1.3389684 9.819070e-02           NA #> f_parent_to_M2  0.16099855 0.033691952  4.7785463 6.531136e-05           NA #> f_M1_to_M3      0.27921507 0.269423709  1.0363419 1.565266e-01  0.022978202 #> f_M2_to_M3      0.55641252 0.595119937  0.9349586 1.807707e-01  0.008002509 #> sigma           1.14005399 0.149696423  7.6157731 1.727024e-07  0.826735778 #>                      Upper #> parent_0       78.35255358 #> k_parent        0.13084582 #> k_M1            6.23970946 #> k_M2            0.09843260 #> k_M3            0.04333992 #> f_parent_to_M1          NA #> f_parent_to_M2          NA #> f_M1_to_M3      0.86450778 #> f_M2_to_M3      0.99489895 #> sigma           1.45337221   mkinerrmin(f_soil) #>             err.min n.optim df #> All data 0.09649963       9 20 #> parent   0.04721283       2  6 #> M1       0.26551208       2  5 #> M2       0.20327575       2  5 #> M3       0.05196550       3  4   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"transformations intended map parameters take restricted values full scale real numbers. kinetic rate constants parameters can take positive values, simple log transformation used. compositional parameters, formations fractions always sum 1 can negative, ilr transformation used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"","code":"transform_odeparms(   parms,   mkinmod,   transform_rates = TRUE,   transform_fractions = TRUE )  backtransform_odeparms(   transparms,   mkinmod,   transform_rates = TRUE,   transform_fractions = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"parms Parameters kinetic models used differential equations. mkinmod kinetic model class mkinmod, containing names model variables needed grouping formation fractions ilr transformation, parameter names information pathway sink included model. transform_rates Boolean specifying kinetic rate constants transformed model specification used fitting better compliance assumption normal distribution estimator. TRUE, also alpha beta parameters FOMC model log-transformed, well k1 k2 rate constants DFOP HS models break point tb HS model. transform_fractions Boolean specifying formation fractions constants transformed model specification used fitting better compliance assumption normal distribution estimator. default (TRUE) transformations. g parameter DFOP model also seen fraction. single fraction transformed (g parameter DFOP single target variable e.g. single metabolite plus pathway sink), logistic transformation used stats::qlogis(). cases, .e. two formation fractions need transformed whose sum exceed one, ilr transformation used. transparms Transformed parameters kinetic models used fitting procedure.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"vector transformed backtransformed parameters","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"transformation sets formation fractions fragile, supposes ordering components forward backward transformation. problem internal use mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"","code":"SFO_SFO <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = TRUE),   m1 = list(type = \"SFO\"), use_of_ff = \"min\") #> Temporary DLL for differentials generated and loaded  # Fit the model to the FOCUS example dataset D using defaults FOCUS_D <- subset(FOCUS_2006_D, value != 0) # remove zero values to avoid warning fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) fit.s <- summary(fit) # Transformed and backtransformed parameters print(fit.s$par, 3) #>                   Estimate Std. Error Lower  Upper #> parent_0             99.60     1.5702 96.40 102.79 #> log_k_parent_sink    -3.04     0.0763 -3.19  -2.88 #> log_k_parent_m1      -2.98     0.0403 -3.06  -2.90 #> log_k_m1_sink        -5.25     0.1332 -5.52  -4.98 #> sigma                 3.13     0.3585  2.40   3.85 print(fit.s$bpar, 3) #>               Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 102.7931 #> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04103   0.0560 #> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04678   0.0551 #> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069 #> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549  # \\dontrun{ # Compare to the version without transforming rate parameters (does not work # with analytical solution, we get NA values for m1 in predictions) fit.2 <- mkinfit(SFO_SFO, FOCUS_D, transform_rates = FALSE,   solution_type = \"deSolve\", quiet = TRUE) fit.2.s <- summary(fit.2) print(fit.2.s$par, 3) #>               Estimate Std. Error    Lower    Upper #> parent_0      99.59848    1.57022 96.40384 1.03e+02 #> k_parent_sink  0.04792    0.00365  0.04049 5.54e-02 #> k_parent_m1    0.05078    0.00205  0.04661 5.49e-02 #> k_m1_sink      0.00526    0.00070  0.00384 6.69e-03 #> sigma          3.12550    0.35852  2.39609 3.85e+00 print(fit.2.s$bpar, 3) #>               Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 1.03e+02 #> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04049 5.54e-02 #> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04661 5.49e-02 #> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00384 6.69e-03 #> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609 3.85e+00 # }  initials <- fit$start$value names(initials) <- rownames(fit$start) transformed <- fit$start_transformed$value names(transformed) <- rownames(fit$start_transformed) transform_odeparms(initials, SFO_SFO) #>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink  #>        100.750000         -2.302585         -2.301586         -2.300587  backtransform_odeparms(transformed, SFO_SFO) #>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink  #>      100.7500        0.1000        0.1001        0.1002   # \\dontrun{ # The case of formation fractions (this is now the default) SFO_SFO.ff <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = TRUE),   m1 = list(type = \"SFO\"),   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_D, quiet = TRUE) fit.ff.s <- summary(fit.ff) print(fit.ff.s$par, 3) #>                 Estimate Std. Error  Lower  Upper #> parent_0         99.5985     1.5702 96.404 102.79 #> log_k_parent     -2.3157     0.0409 -2.399  -2.23 #> log_k_m1         -5.2475     0.1332 -5.518  -4.98 #> f_parent_qlogis   0.0579     0.0893 -0.124   0.24 #> sigma             3.1255     0.3585  2.396   3.85 print(fit.ff.s$bpar, 3) #>                Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0       99.59848    1.57022   63.43 2.30e-36 96.40383 102.7931 #> k_parent        0.09870    0.00403   24.47 4.96e-23  0.09082   0.1073 #> k_m1            0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069 #> f_parent_to_m1  0.51448    0.02230   23.07 3.10e-22  0.46912   0.5596 #> sigma           3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549 initials <- c(\"f_parent_to_m1\" = 0.5) transformed <- transform_odeparms(initials, SFO_SFO.ff) backtransform_odeparms(transformed, SFO_SFO.ff) #> f_parent_to_m1  #>            0.5   # And without sink SFO_SFO.ff.2 <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = FALSE),   m1 = list(type = \"SFO\"),   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_D, quiet = TRUE) fit.ff.2.s <- summary(fit.ff.2) print(fit.ff.2.s$par, 3) #>              Estimate Std. Error Lower Upper #> parent_0        84.79      3.012 78.67 90.91 #> log_k_parent    -2.76      0.082 -2.92 -2.59 #> log_k_m1        -4.21      0.123 -4.46 -3.96 #> sigma            8.22      0.943  6.31 10.14 print(fit.ff.2.s$bpar, 3) #>          Estimate se_notrans t value   Pr(>t)   Lower  Upper #> parent_0  84.7916    3.01203   28.15 1.92e-25 78.6704 90.913 #> k_parent   0.0635    0.00521   12.19 2.91e-14  0.0538  0.075 #> k_m1       0.0148    0.00182    8.13 8.81e-10  0.0115  0.019 #> sigma      8.2229    0.94323    8.72 1.73e-10  6.3060 10.140 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an mkinfit model with different arguments — update.mkinfit","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"function return updated mkinfit object. fitted degradation model parameters old fit used starting values updated fit. Values specified 'parms.ini' /'state.ini' override starting values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"","code":"# S3 method for class 'mkinfit' update(object, ..., evaluate = TRUE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"object mkinfit object updated ... Arguments mkinfit replace arguments original call. Arguments set NULL remove arguments given original call evaluate call evaluated returned call","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"","code":"# \\dontrun{ fit <- mkinfit(\"SFO\", subset(FOCUS_2006_D, value != 0), quiet = TRUE) parms(fit) #>    parent_0    k_parent       sigma  #> 99.44423885  0.09793574  3.39632469  plot_err(fit)  fit_2 <- update(fit, error_model = \"tc\") parms(fit_2) #>     parent_0     k_parent    sigma_low     rsd_high  #> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02  plot_err(fit_2)  # }"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-1-2-10","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 1.2.10 (unreleased)","text":"‘R/{saem,endpoints,plot.mixed.mmkin}.R’: function ‘saem()’ gains argument ‘center_covariates’. functions ‘parms’, ‘endpoints’ ‘plot.mixed.mmkin’ adapted, user can still specify covariate values original, uncentered scale even covariate centered.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-1-2-10","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 1.2.10 (unreleased)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Make parent model selection robust ‘R/mkinpredict.R’: Predictions fits using solution type “deSolve” previously incorrect case observed data time zero (GitHub issue #16).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-1-2-9","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 1.2.9 (2024-02-13)","text":"‘R/plot.mixed.R’: Support 25 datasets ‘R/mkinfit.R’: Support passing observed data ‘tibble’ ‘R/parplot.R’: Support multistart objects covariate models filter negative values scaled parameters (warning) plotting. ’R/create_deg_func.R: Make sure reversible reactions specified case two observed variables, supported","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-128-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.8 (unreleased)","title":"mkin 1.2.8 (unreleased)","text":"‘R/{mhmkin,status}.R’: Deal ‘saem’ fits fail updating ‘mhmkin’ object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-127-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.7 (unreleased)","title":"mkin 1.2.7 (unreleased)","text":"‘R/illparms.R’: Fix bug prevented ill-defined random effect found one random effect model. Also add test .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-126-2023-10-14","dir":"Changelog","previous_headings":"","what":"mkin 1.2.6 (2023-10-14)","title":"mkin 1.2.6 (2023-10-14)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Fix erroneous call ‘endpoints()’ function","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-125-2023-08-09","dir":"Changelog","previous_headings":"","what":"mkin 1.2.5 (2023-08-09)","title":"mkin 1.2.5 (2023-08-09)","text":"‘vignettes/mesotrione_parent_2023.rnw’: Prebuilt vignette showing covariate modelling can done relevant parent degradation models. ‘inst/testdata/mesotrione_soil_efsa_2016}.xlsx’: Another example spreadsheets use ‘read_spreadsheet()’, featuring pH dependent degradation R/illparms.R: Fix detection ill-defined slope error model parameters case estimate negative","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-124-2023-05-19","dir":"Changelog","previous_headings":"","what":"mkin 1.2.4 (2023-05-19)","title":"mkin 1.2.4 (2023-05-19)","text":"R/endpoints.R: Fix calculation endpoints user specified covariate values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-1231-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.3.1 (unreleased)","title":"mkin 1.2.3.1 (unreleased)","text":"Small fixes get online docs right (example code R/hierarchical_kinetics, cluster setup cyantraniliprole dmta pathway vignettes, graphics model comparison multistart vignette), rebuild online docs","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-123-2023-04-17","dir":"Changelog","previous_headings":"","what":"mkin 1.2.3 (2023-04-17)","title":"mkin 1.2.3 (2023-04-17)","text":"‘R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R’: Calculate parameters endpoints plot population curves specific covariate values, specific percentiles covariate values used saem fits. Depend current deSolve version possibility avoid resolving symbols shared library (compiled models) , thanks Thomas Petzoldt. ‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Start new cluster creating model stored user specified location, otherwise symbols found worker processes. ‘tests/testthat/test_compiled_symbols.R’: new tests control problems may introduced possibility use pre-resolved symbols. ‘R/mkinerrmin.R’: Fix typo subset (use = instead ==), thanks Sebastian Meyer spotting work R 4.3.0.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-122-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.2 (unreleased)","title":"mkin 1.2.2 (unreleased)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics’: R markdown template facilitate application hierarchical kinetic models. ‘inst/testdata/{cyantraniliprole_soil_efsa_2014,lambda-cyhalothrin_soil_efsa_2014}.xlsx’: Example spreadsheets use ‘read_spreadsheet()’. ‘R/mhmkin.R’: Allow ‘illparms.mhmkin’ object list suitable dimensions value argument ‘no_random_effects’, making possible exclude random effects ill-defined simpler variants set degradation models. Remove possibility exclude random effects based separate fits, work well. ‘R/summary.saem.mmkin.R’: List initial parameter values summary, including random effects error model parameters. Avoid redundant warnings occurred calculation correlations fixed effects case Fisher information matrix inverted. List correlations random effects specified user covariance model. ‘R/parplot.R’: Possibility select top ‘llquant’ fraction fits parameter plots, improved legend text. ‘R/illparms.R’: Also check confidence intervals slope parameters covariate models include zero. implemented fits obtained saemix backend. ‘R/parplot.R’: Make function work also case multistart runs failed. ‘R/intervals.R’: Include correlations random effects model case .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-121-2022-11-19","dir":"Changelog","previous_headings":"","what":"mkin 1.2.1 (2022-11-19)","title":"mkin 1.2.1 (2022-11-19)","text":"‘{data,R}/ds_mixed.rda’: Include test data package instead generating ‘tests/testthat/setup_script.R’. Refactor generating code make consistent update tests. ‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect DFOP fit ‘saem’, attempt avoid platform dependence surfaced Fedora systems CRAN check farm ‘tests/testthat/test_mixed.R’: Round parameters found saemix two significant digits printing, also help avoid platform dependence tests ‘R/saem.R’: Fix bug prevented ‘error.ini’ passed ‘saemix_model’, set default c(1, 1) avoid changing test results ‘R/parplot.R’: Show initial values error model parameters ‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute resulting ‘logLik’ object, order make test_AIC.R succeed current R-devel","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-120-2022-11-17","dir":"Changelog","previous_headings":"","what":"mkin 1.2.0 (2022-11-17)","title":"mkin 1.2.0 (2022-11-17)","text":"‘R/saem.R’: ‘logLik’, ‘update’ ‘anova’ methods ‘saem.mmkin’ objects. ‘R/saem.R’: Automatic estimation start parameters random effects case mkin transformations, nicely improving convergence reducing problems iterative ODE solutions. ‘R/status.R’: New generic show status information fit array objects methods ‘mmkin’, ‘mhmkin’ ‘multistart’ objects. ‘R/mhmkin.R’: New method performing multiple hierarchical mkin fits one function call, optionally parallel. ‘R/mhmkin.R’: ‘anova.mhmkin’ conveniently comparing resulting fits. ‘R/illparms.R’: New generic show ill-defined parameters methods ‘mkinfit’, ‘mmkin’, ‘saem.mmkin’ ‘mhmkin’ objects. ‘R/multistart.R’: New method testing multiple start parameters hierarchical model fits, function ‘llhist’ new generic ‘parplot’ diagnostics, new generics ‘.best’ ‘best’ extracting fit highest likelihood ‘R/summary.mmkin.R’: Summary method mmkin objects. ‘R/saem.R’: Implement test saemix transformations FOMC HS. Also, error saemix transformations requested supported. ‘R/read_spreadsheet.R’: Conveniently read data spreadsheet file. ‘R/tex_listings.R’: Conveniently include summaries fit objects R markdown documents compiled LaTeX.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-111-2022-07-12","dir":"Changelog","previous_headings":"","what":"mkin 1.1.1 (2022-07-12)","title":"mkin 1.1.1 (2022-07-12)","text":"’R/{mkinmod,mkinpredict}.R: Store DLL information mkinmod objects use information mkinpredict avoid performance regression brought bugfix R 4.2.x. Thanks Tomas Kalibera analysis problem r-package-devel list suggestion fix . ‘vignettes/FOCUS_L.rmd’: Remove outdated note referring failure calculate covariance matrix DFOP L2 dataset. Since 0.9.45.5 covariance matrix available ‘vignettes/web_only/benchmarks.rmd’: Add first benchmark data using laptop system, therefore add CPU showing benchmark results. ‘dimethenamid_2018’: Update example code use saemix ‘CAKE_export’: Check validity map argument, updates ‘saem()’: Slightly improve speed case analytical solution saemix implemented, activate test respective code ‘mean_degparms’: New argument ‘default_log_parms’ makes possible supply surrogate value (default) log parameters fail t-test ‘plot.mixed.mmkin’: Pass frame argument also residual plots, take ‘default_log_parms’ argument ‘mean_degparms’ used constructing approximate population curves, plot population curve last avoid covered data ‘plot.mkinfit’: Respect argument ‘maxabs’ residual plots, make possible give ylim list, row layouts","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mixed-effects-models-1-1-0","dir":"Changelog","previous_headings":"","what":"Mixed-effects models","title":"mkin 1.1.0 (2022-03-14)","text":"Reintroduce interface saemix version 3.0 (now CRAN), particular generic function ‘saem’ generator ‘saem.mmkin’, currently using ‘saemix_model’ ‘saemix_data’, summary plot methods ‘mean_degparms’: New argument ‘test_log_parms’ makes function consider log-transformed parameters untransformed parameters pass t-test certain confidence level. can used obtain plausible starting parameters different mixed-effects model backends ‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ ‘conf.level’ ‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations dimethenamid data. ‘intervals’: Provide method nlme function ‘saem.mmkin’ objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-105-2021-09-15","dir":"Changelog","previous_headings":"","what":"mkin 1.0.5 (2021-09-15)","title":"mkin 1.0.5 (2021-09-15)","text":"‘dimethenamid_2018’: Correct data Borstel soil. five observations Staudenmaier (2013) previously stored “Borstel 2” actually just subset 16 observations “Borstel 1” now simply “Borstel”","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-104-2021-04-20","dir":"Changelog","previous_headings":"","what":"mkin 1.0.4 (2021-04-20)","title":"mkin 1.0.4 (2021-04-20)","text":"plotting functions setting graphical parameters: Use .exit() resetting graphical parameters ‘plot.mkinfit’: Use xlab xlim residual plot show_residuals TRUE ‘mmkin’: Use cores = 1 per default Windows make easier first time users","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-103-2021-02-15","dir":"Changelog","previous_headings":"","what":"mkin 1.0.3 (2021-02-15)","title":"mkin 1.0.3 (2021-02-15)","text":"Review update README, ‘Introduction mkin’ vignette help pages","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-102-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.0.2 (Unreleased)","title":"mkin 1.0.2 (Unreleased)","text":"‘mkinfit’: Keep model names stored ‘mkinmod’ objects, avoiding loss ‘gmkin’","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-101-2021-02-10","dir":"Changelog","previous_headings":"","what":"mkin 1.0.1 (2021-02-10)","title":"mkin 1.0.1 (2021-02-10)","text":"‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code dontrun sections failed current defaults ‘logLik.mkinfit’: Improve example code avoid warnings show convenient syntax ‘mkinresplot’: Re-add Katrin Lindenberger coauthor accidentally removed long ago Remove tests relying non-convergence FOMC fit FOCUS dataset platform dependent (revealed new additional tests CRAN, thanks!) Increase test tolerance parameter comparisons also proved platform dependent","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"general-1-0-0","dir":"Changelog","previous_headings":"","what":"General","title":"mkin 1.0.0 (2021-02-03)","text":"‘mkinmod’ models gain arguments ‘name’ ‘dll_dir’ , conjunction current version ‘inline’ package, make possible still use DLL used fast ODE solutions ‘deSolve’ saving restoring ‘mkinmod’ object. ‘mkindsg’ R6 class groups ‘mkinds’ datasets metadata ‘f_norm_temp_focus’ generic function normalise time intervals using FOCUS method, methods numeric vectors ‘mkindsg’ objects ‘D24_2014’ ‘dimethenamid_2018’ datasets ‘focus_soil_moisture’ FOCUS default soil moisture data ‘update’ method ‘mmkin’ objects ‘transform_odeparms’, ‘backtransform_odeparms’: Use logit transformation solitary fractions like g parameter DFOP model, formation fractions pathway one target variable ‘plot.mmkin’: Add ylab argument, making possible customize y axis label panels left without affecting residual plots. Reduce legend size vertical distance panels ‘plot.mkinfit’: Change default ylab “Observed” “Residue”. Pass xlab residual plot show_residuals TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mixed-effects-models-1-0-0","dir":"Changelog","previous_headings":"","what":"Mixed-effects models","title":"mkin 1.0.0 (2021-02-03)","text":"‘mixed.mmkin’ New container mmkin objects plotting ‘plot.mixed.mmkin’ method ‘plot.mixed.mmkin’ method used ‘nlme.mmkin’ inheriting ‘mixed.mmkin’ (currently virtual) ‘plot’, ‘summary’ ‘print’ methods ‘nlme.mmkin’ objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09503-2020-10-08","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.3 (2020-10-08)","title":"mkin 0.9.50.3 (2020-10-08)","text":"‘parms’: Add method mmkin objects ‘mmkin’ ‘confint(method = ’profile’): Use cores detected parallel::detectCores() per default ‘confint(method = ’profile’): Choose accuracy based ‘rel_tol’ argument, relative bounds obtained quadratic approximation ‘mkinfit’: Make ‘use_of_ff’ = “max” also default models specified using short names like “SFO” “FOMC” ‘mkinfit’: Run ‘stats::shapiro.test()’ standardized residuals warn p < 0.05 ‘mkinfit’: ‘error_model_algorithm’ = ‘d_3’ fail direct fitting fails, reports results threestep algorithm returned ‘mmkin’: fail one fits fails, assign try-error respective position mmkin object ‘mkinfit’: Ignore components state.ini correspond state variables model ‘endpoints’: Back-calculate DT50 value DT90 also biphasic models DFOP, HS SFORB","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09502-2020-05-12","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.2 (2020-05-12)","title":"mkin 0.9.50.2 (2020-05-12)","text":"Increase tolerance platform specific test results Solaris test machine CRAN Updates corrections (using spelling package) documentation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09501-2020-05-11","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.1 (2020-05-11)","title":"mkin 0.9.50.1 (2020-05-11)","text":"Support SFORB formation fractions ‘mkinmod’: Make ‘use_of_ff’ = “max” default Improve performance ) avoiding expensive calls cost function like merge() data.frame(), b) implementing analytical solutions SFO-SFO DFOP-SFO","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-094911-2020-04-20","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.11 (2020-04-20)","title":"mkin 0.9.49.11 (2020-04-20)","text":"Increase test tolerance make pass CRAN check machines","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-094910-2020-04-18","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.10 (2020-04-18)","title":"mkin 0.9.49.10 (2020-04-18)","text":"‘nlme.mmkin’: nlme method mmkin row objects associated S3 class print, plot, anova endpoint methods ‘mean_degparms, nlme_data, nlme_function’: Three new functions facilitate building nlme models mmkin row objects ‘endpoints’: Don’t return SFORB list component ’s empty. reduces distraction complies documentation Article compiled models: Add platform specific code suppress warnings zero values removed FOCUS D dataset ‘plot.mmkin’: Add argument ‘standardized’ avoid warnings occurred passed part additional arguments captured dots (…) ‘summary.mkinfit’: Add AIC, BIC log likelihood summary","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09499-2020-03-31","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.9 (2020-03-31)","title":"mkin 0.9.49.9 (2020-03-31)","text":"‘mkinmod’: Use pkgbuild::has_compiler instead Sys.(‘gcc’), latter often fail even Rtools installed ‘mkinds’: Use roxygen documenting fields methods R6 class","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09498-2020-01-09","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.8 (2020-01-09)","title":"mkin 0.9.49.8 (2020-01-09)","text":"‘aw’: Generic function calculating Akaike weights, methods mkinfit objects mmkin columns ‘loftest’: Add lack--fit test ‘plot_res’, ‘plot_sep’ ‘mkinerrplot’: Add possibility show standardized residuals make default fits error models ‘const’ ‘lrtest.mkinfit’: Improve naming compared fits case fixed parameters ‘confint.mkinfit’: Make quadratic approximation default, likelihood profiling takes lot time, especially fit three parameters","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09497-2019-11-01","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.7 (2019-11-01)","title":"mkin 0.9.49.7 (2019-11-01)","text":"Fix bug introduced 0.9.49.6 occurred direct optimisation yielded higher likelihood three-step optimisation d_3 algorithm, caused fitted parameters three-step optimisation returned instead parameters direct optimisation Add ‘nobs’ method mkinfit objects, enabling default ‘BIC’ method stats package. Also, add ‘BIC’ method mmkin column objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09496-2019-10-31","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.6 (2019-10-31)","title":"mkin 0.9.49.6 (2019-10-31)","text":"Implement likelihood ratio test method ‘lrtest’ lmtest package Add ‘update’ method mkinfit objects remembers fitted parameters appropriate Add ‘residuals’ method mkinfit objects supports scaling based error model Fix bug ‘mkinfit’ prevented summaries objects fitted fixed parameters generated Add ‘parms’ ‘confint’ methods mkinfit objects. Confidence intervals based quadratic approximation summary, based profile likelihood Move long-running tests tests/testthat/slow separate test log. currently take around 7 minutes system ‘mkinfit’: Clean code return functions calculate log-likelihood sum squared residuals Vignette ‘twa.html’: Add maximum time weighted average formulas hockey stick model Support frameless plots (‘frame = FALSE’) Support suppress chi2 error level (‘show_errmin = FALSE’) ‘plot.mmkin’ Update README introductory vignette Report ‘OLS’ error_model_algorithm summary case default error_model (‘const’) used Support summarizing ‘mkinfit’ objects generated versions < 0.9.49.5","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09495-2019-07-04","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.5 (2019-07-04)","title":"mkin 0.9.49.5 (2019-07-04)","text":"Several algorithms minimization negative log-likelihood non-constant error models (two-component variance variable). case error model constant variance, least squares used stable. default algorithm ‘d_3’ tries direct minimization three-step procedure, returns model highest likelihood. argument ‘reweight.method’ mkinfit mmkin now obsolete, use ‘error_model’ ‘error_model_algorithm’ instead Add test checks get best known AIC parent fits 12 test datasets. Add test datasets purpose. New function ‘mkinerrplot’. function also used residual plots ‘plot.mmkin’ argument ‘resplot = “errmod”’ given, ‘plot.mkinfit’ ‘show_errplot’ set TRUE. Remove dependency FME, use nlminb optimisation (‘Port’ algorithm). remember cases one optimisation algorithms preferable, except sometime used Levenberg-Marquardt speed cases expect get trapped local minimum. Use numDeriv package calculate hessians. results slightly different confidence intervals, takes bit longer, apparently robust Add simple benchmark vignette document impact performance. code manual weighting removed. functionality might get added later time. time , please use earlier version, e.g. 0.9.48.1 want manual weighting. fitting time reported summary now includes time used calculation hessians Adapt tests Fix error FOCUS chi2 error level calculations occurred parameters specified parms.ini model. warning already issued, fitting parallel via mmkin go unnoticed. Add example datasets obtained risk assessment reports published European Food Safety Agency.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09481-2019-03-04","dir":"Changelog","previous_headings":"","what":"mkin 0.9.48.1 (2019-03-04)","title":"mkin 0.9.48.1 (2019-03-04)","text":"Add function ‘logLik.mkinfit’ makes possible calculate AIC mkinfit objects Add function ‘AIC.mmkin’ make easy compare columns mmkin objects ‘add_err’: Respect argument giving number replicates synthetic dataset ‘max_twa_parent’: Support maximum time weighted average concentration calculations hockey stick (HS) model ‘mkinpredict’: Make function generic create method mkinfit objects ‘mkinfit’: Improve correctness fitted two component error model fitting mean absolute deviance observation observed values, weighting current two-component error model ‘tests/testthat/test_irls.R’: Test components error model used generate data can reproduced moderate accuracy Add function ‘CAKE_export’ facilitate cross-checking results Implement logistic model (tested parent fits) ‘nafta’: Add evaluations according NAFTA guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09475-2018-09-14","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.5 (2018-09-14)","title":"mkin 0.9.47.5 (2018-09-14)","text":"Make two-component error model stop cases inadequate avoid nls crashes windows Move two vignettes location built CRAN (avoid NOTES long execution times) Exclude example code testing CRAN avoid NOTES long execution times","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09473","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.3","title":"mkin 0.9.47.3","text":"‘mkinfit’: Improve fitting error model reweight.method = ‘tc’. Add ‘manual’ possible arguments ‘weight’ Test FOCUS_2006_C can evaluated DFOP reweight.method = ‘tc’","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09472-2018-07-19","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.2 (2018-07-19)","title":"mkin 0.9.47.2 (2018-07-19)","text":"‘sigma_twocomp’: Rename ‘sigma_rl’ ‘sigma_twocomp’ Rocke Lorenzato model assumes lognormal distribution large y. Correct references Rocke Lorenzato model accordingly. ‘mkinfit’: Use 1.1 starting value N parameter IORE models obtain convergence difficult cases. Show parameter names ‘trace_parms’ ‘TRUE’.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09471-2018-02-06","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.1 (2018-02-06)","title":"mkin 0.9.47.1 (2018-02-06)","text":"Skip tests CRAN winbuilder avoid timeouts ‘test_data_from_UBA_2014’: Added list datasets containing experimental data used expertise 2014 ‘mkinfit’: Added iterative reweighting method ‘tc’ using two-component error model Rocke Lorenzato. NA values data returned . ‘mkinfit’: Work around bug current FME version prevented convergence message returned case non-convergence. ‘summary.mkinfit’: Improved output regarding weighting method. predictions returned NA values model (see ). ‘summary.mkinfit’: Show versions mkin R used fitting (ones used summary) fit generated mkin >= 0.9.47.1","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09463-2017-11-16","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.3 (2017-11-16)","title":"mkin 0.9.46.3 (2017-11-16)","text":"README.md, vignettes/mkin.Rmd: URLs updated synthetic_data_for_UBA: Add code used generate data interest reproducibility","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09462-2017-10-10","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.2 (2017-10-10)","title":"mkin 0.9.46.2 (2017-10-10)","text":"Converted vignette FOCUS_Z tex/pdf markdown/html DESCRIPTION: Add ORCID","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09461-2017-09-14","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.1 (2017-09-14)","title":"mkin 0.9.46.1 (2017-09-14)","text":"plot.mkinfit: Fix scaling residual plots case separate plots observed variable plot.mkinfit: Use data points fitted curve y axis scaling case separate plots observed variable Documentation updates","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-0946-2017-07-24","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46 (2017-07-24)","title":"mkin 0.9.46 (2017-07-24)","text":"Remove test_FOMC_ill-defined.R platform dependent","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09452-2017-07-24","dir":"Changelog","previous_headings":"","what":"mkin 0.9.45.2 (2017-07-24)","title":"mkin 0.9.45.2 (2017-07-24)","text":"Rename twa max_twa_parent avoid conflict twa pfm package Update URLs documentation Limit test code one core pass windows Switch microbenchmark rbenchmark former supported platforms","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-45-1","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9.45.1 (2016-12-20)","text":"twa function, calculating maximum time weighted average concentrations parent (SFO, FOMC DFOP).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-45","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.45 (2016-12-08)","text":"plot.mkinfit plot.mmkin: plotting device tikz, LaTeX markup used chi2 error graphs. Use pkgdown, successor staticdocs generating static HTML documentation. Include example output graphs also dontrun sections. plot.mkinfit: Plotting fail compiled model available, e.g. removed temporary directory. case, uncompiled model now used plotting","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-44","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9.44 (2016-06-29)","text":"test test_FOMC_ill-defined failed several architectures, test now skipped","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-43","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9.43 (2016-06-28)","text":"title changed Kinetic evaluations chemical degradation data plot.mkinfit: Add possibility show fits (residual plots requested) separately observed variables plot.mkinfit: Add possibility show chi2 error levels plot, similar way shown plot.mmkin plot_sep: Add function convenience wrapper plotting observed variables mkinfit objects separately, chi2 error values residual plots. Vignettes: main vignette mkin converted R markdown updated. vignettes also updated show current improved functionality. function add_err added package, making easy generate simulated data using error model based normal distribution","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-43","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.43 (2016-06-28)","text":"Remove outdated reference inline package compiled_models vignette mkinfit: error cases fit converges, Jacobian untransformed model cost can estimated. Give warning instead return NA t-test results. summary.mkinfit: Give warning message covariance matrix can obtained. test added containing corresponding edge case check warnings correctly issued fit terminate. plot.mmkin: Round chi2 error value three significant digits, instead two decimal digits. mkinfit: Return err values used weighted fits column named err. Also include inverse weights column value observed data used, returned observed data component mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-43","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9.43 (2016-06-28)","text":"endpoints: name substance degrading metabolite (e.g. parent compound) used model formulation ended letter f, rate parameters listed formation fractions mixed names. also appear summary. mkinfit: Check observed variables checking user tried fix formation fractions fitting using ilr transformation. plot.mmkin: Set plot margins correctly, also case single fit plotted, main title placed reasonable way. plot.mkinfit: Correct default values col_obs, pch_obs lty_obs case obs_vars specified.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-42","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9.42 (2016-03-25)","text":"Add argument from_max_mean mkinfit, fitting decline maximum observed value models single observed variable","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-42","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.42 (2016-03-25)","text":"Add plots compiled_models vignette Give explanatory error message mkinmod fails due missing definition target variable print.mkinmod(): Improve formatting printing mkinmod model definitions","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-41","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-41 (2015-11-09)","text":"Add R6 class mkinds representing datasets printing method Add printing method mkinmod objects Make possible specify arbitrary strings names compounds mkinmod, show plot Use index.r file group help topics static documentation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-41","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-41 (2015-11-09)","text":"print.summary.mkinfit(): Avoid error occurred printing summaries generated mkin versions 0.9-36","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-40","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-40 (2015-07-21)","text":"endpoints(): DFOP SFORB models, optimize() used, make use fact DT50 must DT50_k1 DT50_k2 (DFOP) DT50_b1 DT50_b2 (SFORB), optimize() sometimes find minimum. Likewise finding DT90 values. Also fit log scale make function efficient.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"internal-changes-0-9-40","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"mkin 0.9-40 (2015-07-21)","text":"DESCRIPTION, NAMESPACE, R/*.R: Import () stats, graphics methods packages, qualify function calls non-base packages installed R avoid NOTES made R CMD check –-cran upcoming R versions.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-39","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-39 (2015-06-26)","text":"New function mmkin(): function takes character vector model shorthand names, alternatively list mkinmod models, well list dataset main arguments. returns matrix mkinfit objects, row model column dataset. subsetting method single brackets available. Fitting models parallel using parallel package supported. New function plot.mmkin(): Plots single-row single-column mmkin objects including residual plots.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-39","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-39 (2015-06-26)","text":"mkinparplot(): Fix x axis scaling rate constants formation fractions got confused introduction t-values transformed parameters.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-38","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-38 (2015-06-24)","text":"vignettes/compiled_models.html: Show performance improvement factor actually obtained building vignette, well mkin version, system info CPU model used building vignette. GNUMakefile,vignettes/*: Clean vignette generation include table contents HTML vignettes.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-38","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-38 (2015-06-24)","text":"mkinmod(): generating C code derivatives, declare time variable needed remove ‘-W--unused-variable’ compiler flag C compiler used CRAN checks Solaris know .","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-36","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-36 (2015-06-21)","text":"summary.mkinfit(): one-sided t-test significant difference untransformed parameters zero now always shown, based assumption normal distribution estimators untransformed parameters. Use caution, assumption unrealistic e.g. rate constants nonlinear kinetic models. compiler (gcc) installed, use version differential equation model compiled C code, huge performance boost models deSolve method works. mkinmod(): Create list component $cf (class CFuncList) list returned mkinmod, version can compiled autogenerated C code (see ). mkinfit(): Set default solution_type deSolve compiled version model present, except analytical solution possible.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-36","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-36 (2015-06-21)","text":"Added simple showcase vignette evaluation FOCUS example dataset D","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-35","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-35 (2015-05-15)","text":"Switch RUnit testthat testing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-35","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-35 (2015-05-15)","text":"mkinparplot(): Avoid warnings occurred confidence intervals available summary fit print.summary.mkinfit(): Fix printing summary case number iterations available NAMESPACE: export S3 methods plot.mkinfit, summary.mkinfit print.summary.mkinfit satisfy R CMD check R-devel mkinparplot(): Avoid warning R CMD check undeclared global variable Lower","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-35","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-35 (2015-05-15)","text":"mkinfit(): Report successful termination quiet = FALSE. helpful difficult problems fitted reweight.method = obs, progress often indicated reweighting. first test using results established expertise written German Federal Environmental Agency (UBA) added. Add synthetic datasets generated expertise written German Federal Environmental Agency UBA Add tests based datasets","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-34","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-34 (2014-11-22)","text":"Add convenience function mkinsub() creating lists used mkinmod() Add possibility fit indeterminate order rate equation (IORE) models using analytical solution (parent ) numeric solution. Paths IORE compounds metabolites supported using formation fractions (use_of_ff = ‘max’). Note numerical solution (method.ode = ‘deSolve’) IORE differential equations sometimes fails due numerical problems. Switch using Port algorithm (using model/trust region approach) per default. needing iterations Levenberg-Marquardt algorithm previously used per default, less sensitive starting parameters.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-34","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-34 (2014-11-22)","text":"formatting differential equations summary improved Always include 0 y axis plotting fit","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-33","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-33 (2014-10-22)","text":"initial value (state.ini) observed variable highest observed residue set 100 case time zero observation state.ini = \"auto\" basic unit test mkinerrmin() written","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-33","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-33 (2014-10-22)","text":"mkinfit(): internally fitted parameter g named g_ilr even transform_fractions=FALSE mkinfit(): initial value (state.ini) parent compound set parent () variable highest value observed data. mkinerrmin(): checking degrees freedom metabolites, check time zero value fixed instead checking observed value zero. ensures correct calculation degrees freedom also cases metabolite residue time zero greater zero. plot.mkinfit(): Avoid warning message using first component ylim occurred ylim specified explicitly","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-33","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-33 (2014-10-22)","text":"formatting differential equations summary improved wrapping overly long lines FOCUS_Z vignette rebuilt improvement using width 70 avoid output outside grey area print.summary.mkinfit(): Avoid warning occurred gmkin showed summaries initial fits without iterations mkinfit(): Avoid warning occurred summarising fit performed maxitmodFit = 0 done gmkin configuring new fits.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-32","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-32 (2014-07-24)","text":"number degrees freedom difficult define case ilr transformation formation fractions. Now source compartment number ilr parameters (=number optimised parameters) divided number pathways metabolites (=number affected data series) leads fractional degrees freedom cases. default initial value first state value now taken mean observations time zero, available. kinetic model can alternatively specified shorthand name parent degradation models, e.g. SFO, DFOP. Optimisation method, number model evaluations time elapsed optimisation given summary mkinfit objects. maximum number iterations optimisation algorithm can specified using argument maxit.modFit mkinfit function. mkinfit gives warning fit converge (apply SANN method). warning included summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-32","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-32 (2014-07-24)","text":"Avoid plotting artificial 0 residual time zero mkinresplot determination degrees freedom mkinerrmin, formation fractions accounted multiple times case parallel formation metabolites. See new feature described solution. transform_rates=FALSE mkinfit now also works FOMC HS models. Initial values formation fractions set cases. warning given fit converge method default Levenberg-Marquardt method Marq used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-32","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-32 (2014-07-24)","text":"Vignettes rebuilt reflect changes summary method. Algorithm Pseudo excluded needs user-defined parameter limits supported. Algorithm Newton excluded different way specify maximum number iterations appear provide additional benefits.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-31","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-31 (2014-07-14)","text":"internal renaming optimised parameters Version 0.9-30 led errors determination degrees freedom chi2 error level calulations mkinerrmin() used summary function.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-30","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-30 (2014-07-11)","text":"now possible use formation fractions combination turning sink mkinmod().","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-30","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-30 (2014-07-11)","text":"original transformed parameters now different names (e.g. k_parent log_k_parent. also differ many formation fractions pathway sink. order information blocks print.summary.mkinfit.R() ordered logical way.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-30","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-30 (2014-07-11)","text":"vignette FOCUS_Z simplified use formation fractions turning sink, slightly amended use new versions DT50 values calculated since mkin 0.9-29. vignettes rebuilt reflect changes. ChangeLog renamed NEWS.md entries converted markdown syntax compatible tools::news() function built R. test suite overhauled. Tests DFOP SFORB models dataset FOCUS_2006_A removed, much dependent optimisation algorithm /starting parameters, dataset SFO (compare kinfit vignette). Also, Schaefer complex case can now fitted using formation fractions, ‘Port’ optimisation method also fit A2 way published Piacenza paper. checks introduced mkinfit(), leading warnings stopping execution unsupported combinations methods parameters requested.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-29-2014-06-27","dir":"Changelog","previous_headings":"","what":"mkin 0.9-29 (2014-06-27)","title":"mkin 0.9-29 (2014-06-27)","text":"R/mkinresplot.R: Make possible specify xlim R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values FOMC, DFOP, HS SFORB. Avoid calculation formation fractions rate constants directly fitted","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-28-2014-05-20","dir":"Changelog","previous_headings":"","what":"mkin 0.9-28 (2014-05-20)","title":"mkin 0.9-28 (2014-05-20)","text":"backtransform confidence intervals formation fractions one compound formed, parameters define pathways set Add historical remarks background main package vignette Correct ‘isotropic’ ‘isometric’ ilr transformation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-27-2014-05-10","dir":"Changelog","previous_headings":"","what":"mkin 0.9-27 (2014-05-10)","title":"mkin 0.9-27 (2014-05-10)","text":"Fork GUI separate package gmkin DESCRIPTION, NAMESPACE, TODO: Adapt add copyright information Remove files belonging GUI Possibility fit without parameter transformations, using bounds implemented FME Add McCall 2,4,5-T dataset Enable selection observed variables plotting Add possibility show residual plot plot.mkinfit R/mkinparplot.R, man/mkinparplot.Rd: plot parameters confidence intervals Change vignette format Sweave knitr Split examples vignette FOCUS_L FOCUS_Z Remove warning constant formation fractions mkinmod based misconception Restrict unit test Schaefer data parent primary metabolites formation fraction DT50 A2 highly correlated passing test platform dependent. example, test fails 1 14 platforms CRAN today. Add Eurofins Regulatory AG copyright notices Import FME deSolve instead depending clean startup Add starter function GUI: gmkin() Change format workspace files gmkin can distributed documented package Add gmkin workspace datasets FOCUS_2006_gmkin FOCUS_2006_Z_gmkin","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-24-2013-11-06","dir":"Changelog","previous_headings":"","what":"mkin 0.9-24 (2013-11-06)","title":"mkin 0.9-24 (2013-11-06)","text":"Bugfix re-enabling fixing combination initial values state variables Default values kinetic rate constants 0.1 “salted” small increment avoid numeric artefacts eigenvalue based solutions Backtransform fixed ODE parameters summary","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-22-2013-10-26","dir":"Changelog","previous_headings":"","what":"mkin 0.9-22 (2013-10-26)","title":"mkin 0.9-22 (2013-10-26)","text":"Get rid optimisation step mkinerrmin - unnecessary. Thanks KinGUII inspiration - actually equation 6-2 FOCUS kinetics p. 91 overlooked originally Fix plot.mkinfit passed graphical arguments like main solver use plot=TRUE mkinfit() example first successful fits simple GUI Fix iteratively reweighted least squares case many metabolites Unify naming initial values state variables Unify naming dataframes optimised fixed parameters summary Show weighting method residuals summary Correct output data case manual weighting Implement IRLS assuming different variances observed variables use 0 values time zero chi2 error level calculations. way done KinGUII makes sense. impact chi2 error levels output. Generally seem lower metabolites now, presumably mean observed values higher detailed list changes mkin source please consult commit history http://github.com/jranke/mkin","code":""}]
+[{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l1","dir":"Articles","previous_headings":"","what":"Laboratory Data L1","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L1 FOCUS kinetics report, p. 284: use assumptions simple first order (SFO), case declining rate constant time (FOMC) case two different phases kinetics (DFOP). detailed discussion models, please see FOCUS kinetics report. Since mkin version 0.9-32 (July 2014), can use shorthand notation like \"SFO\" parent degradation models. following two lines fit model produce summary report model fit. covers numerical analysis given FOCUS report. plot fit obtained plot function mkinfit objects.  residual plot can easily obtained  comparison, FOMC model fitted well, χ2\\chi^2 error level checked.  get warning default optimisation algorithm Port converge, indication model overparameterised, .e. contains many parameters ill-defined consequence. fact, due higher number parameters, lower number degrees freedom fit, χ2\\chi^2 error level actually higher FOMC model (3.6%) SFO model (3.4%). Additionally, parameters log_alpha log_beta internally fitted model excessive confidence intervals, span 25 orders magnitude (!) backtransformed scale alpha beta. Also, t-test significant difference zero indicate significant difference, p-values greater 0.1, finally, parameter correlation log_alpha log_beta 1.000, clearly indicating model overparameterised. χ2\\chi^2 error levels reported Appendix 3 Appendix 7 FOCUS kinetics report rounded integer percentages partly deviate one percentage point results calculated mkin. reason known. However, mkin gives χ2\\chi^2 error levels kinfit package calculation routines kinfit package extensively compared results obtained KinGUI software, documented kinfit package vignette. KinGUI first widely used standard package field. Also, calculation χ2\\chi^2 error levels compared KinGUII, CAKE DegKin manager project sponsored German Umweltbundesamt (Ranke 2014).","code":"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) m.L1.SFO <- mkinfit(\"SFO\", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:26 2025  ## Date of summary: Mon May 12 21:54:26 2025  ##  ## Equations: ## d_parent/dt = - k_parent * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 133 model solutions performed in 0.011 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  0.10 deparm ##  ## Starting values for the transformed parameters actually optimised: ##                  value lower upper ## parent_0     89.850000  -Inf   Inf ## log_k_parent -2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC     BIC    logLik ##   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   -2.347    0.03763 -2.428 -2.267 ## sigma           2.780    0.46330  1.792  3.767 ##  ## Parameter correlation: ##                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  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 ## All data   3.424       2  7 ## parent     3.424       2  7 ##  ## Estimated disappearance times: ##         DT50  DT90 ## parent 7.249 24.08 ##  ## Data: ##  time variable observed predicted residual ##     0   parent     88.3    92.471  -4.1710 ##     0   parent     91.4    92.471  -1.0710 ##     1   parent     85.6    84.039   1.5610 ##     1   parent     84.5    84.039   0.4610 ##     2   parent     78.9    76.376   2.5241 ##     2   parent     77.6    76.376   1.2241 ##     3   parent     72.0    69.412   2.5884 ##     3   parent     71.9    69.412   2.4884 ##     5   parent     50.3    57.330  -7.0301 ##     5   parent     59.4    57.330   2.0699 ##     7   parent     47.0    47.352  -0.3515 ##     7   parent     45.1    47.352  -2.2515 ##    14   parent     27.7    24.247   3.4528 ##    14   parent     27.3    24.247   3.0528 ##    21   parent     10.0    12.416  -2.4163 ##    21   parent     10.4    12.416  -2.0163 ##    30   parent      2.9     5.251  -2.3513 ##    30   parent      4.0     5.251  -1.2513 plot(m.L1.SFO, show_errmin = TRUE, main = \"FOCUS L1 - SFO\") mkinresplot(m.L1.SFO, ylab = \"Observed\", xlab = \"Time\") 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\") summary(m.L1.FOMC, data = FALSE) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:26 2025  ## Date of summary: Mon May 12 21:54:26 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 342 model solutions performed in 0.024 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 89.85  state ## alpha     1.00 deparm ## beta     10.00 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  89.850000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ##  ## Warning(s):  ## Optimisation did not converge: ## false convergence (8) ##  ## Results: ##  ##        AIC      BIC    logLik ##   95.88782 99.44931 -43.94391 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error  Lower  Upper ## parent_0     92.47     1.2820 89.720 95.220 ## log_alpha    13.20        NaN    NaN    NaN ## log_beta     15.54        NaN    NaN    NaN ## sigma         2.78     0.4607  1.792  3.768 ##  ## Parameter correlation: ##           parent_0 log_alpha log_beta    sigma ## parent_0  1.000000       NaN      NaN 0.000603 ## log_alpha      NaN         1      NaN      NaN ## log_beta       NaN       NaN        1      NaN ## sigma     0.000603       NaN      NaN 1.000000 ##  ## 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 9.247e+01      NA     NA 89.720 95.220 ## alpha    5.386e+05      NA     NA     NA     NA ## beta     5.633e+06      NA     NA     NA     NA ## sigma    2.780e+00      NA     NA  1.792  3.768 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   3.619       3  6 ## parent     3.619       3  6 ##  ## Estimated disappearance times: ##         DT50  DT90 DT50back ## parent 7.249 24.08    7.249"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l2","dir":"Articles","previous_headings":"","what":"Laboratory Data L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L2 FOCUS kinetics report, p. 287:","code":"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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"sfo-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"SFO fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":", SFO model fitted result plotted. residual plot can obtained simply adding argument show_residuals plot command.  χ2\\chi^2 error level 14% suggests model fit well. also obvious plots fit, included residual plot. FOCUS kinetics report, stated apparent systematic error observed residual plot measured DT90 (approximately day 5), underestimation beyond point. may add difficult judge random nature residuals just three samplings days 0, 1 3. Also, clear priori consistent underestimation approximate DT90 irrelevant. However, can rationalised fact FOCUS fate models generally implement SFO kinetics.","code":"m.L2.SFO <- mkinfit(\"SFO\", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,      main = \"FOCUS L2 - SFO\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"fomc-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"FOMC fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"comparison, FOMC model fitted well, χ2\\chi^2 error level checked.  error level χ2\\chi^2 test passes much lower case. Therefore, FOMC model provides better description data, less experimental error assumed order explain data.","code":"m.L2.FOMC <- mkinfit(\"FOMC\", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE,      main = \"FOCUS L2 - FOMC\") summary(m.L2.FOMC, data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:26 2025  ## Date of summary: Mon May 12 21:54:26 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 239 model solutions performed in 0.015 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 93.95  state ## alpha     1.00 deparm ## beta     10.00 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  93.950000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   61.78966 63.72928 -26.89483 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error    Lower   Upper ## parent_0   93.7700     1.6130 90.05000 97.4900 ## log_alpha   0.3180     0.1559 -0.04149  0.6776 ## log_beta    0.2102     0.2493 -0.36460  0.7850 ## sigma       2.2760     0.4645  1.20500  3.3470 ##  ## Parameter correlation: ##             parent_0  log_alpha   log_beta      sigma ## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 ## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07 ## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.386e-07 ## sigma     -7.436e-09 -1.617e-07 -1.386e-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   93.770  58.120 4.267e-12 90.0500 97.490 ## alpha       1.374   6.414 1.030e-04  0.9594  1.969 ## beta        1.234   4.012 1.942e-03  0.6945  2.192 ## sigma       2.276   4.899 5.977e-04  1.2050  3.347 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   6.205       3  3 ## parent     6.205       3  3 ##  ## Estimated disappearance times: ##          DT50  DT90 DT50back ## parent 0.8092 5.356    1.612"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"dfop-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"DFOP fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"Fitting four parameter DFOP model reduces χ2\\chi^2 error level.  , DFOP model clearly best-fit model dataset L2 based chi^2 error level criterion.","code":"m.L2.DFOP <- mkinfit(\"DFOP\", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,      main = \"FOCUS L2 - DFOP\") summary(m.L2.DFOP, data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:27 2025  ##  ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * ##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) ##            * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 581 model solutions performed in 0.043 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 93.95  state ## k1        0.10 deparm ## k2        0.01 deparm ## g         0.50 deparm ##  ## Starting values for the transformed parameters actually optimised: ##              value lower upper ## parent_0 93.950000  -Inf   Inf ## log_k1   -2.302585  -Inf   Inf ## log_k2   -4.605170  -Inf   Inf ## g_qlogis  0.000000  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   52.36695 54.79148 -21.18347 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##          Estimate Std. Error      Lower     Upper ## parent_0   93.950  9.998e-01    91.5900   96.3100 ## log_k1      3.113  1.849e+03 -4369.0000 4375.0000 ## log_k2     -1.088  6.285e-02    -1.2370   -0.9394 ## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638 ## sigma       1.414  2.886e-01     0.7314    2.0960 ##  ## Parameter correlation: ##            parent_0     log_k1     log_k2   g_qlogis      sigma ## parent_0  1.000e+00  6.763e-07 -8.944e-10  2.665e-01 -1.083e-09 ## log_k1    6.763e-07  1.000e+00  1.112e-04 -2.187e-04 -1.027e-05 ## log_k2   -8.944e-10  1.112e-04  1.000e+00 -7.903e-01  9.464e-09 ## g_qlogis  2.665e-01 -2.187e-04 -7.903e-01  1.000e+00 -1.532e-08 ## sigma    -1.083e-09 -1.027e-05  9.464e-09 -1.532e-08  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  93.9500 9.397e+01 2.036e-12 91.5900 96.3100 ## k1        22.4900 5.533e-04 4.998e-01  0.0000     Inf ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data    2.53       4  2 ## parent      2.53       4  2 ##  ## Estimated disappearance times: ##          DT50  DT90 DT50back DT50_k1 DT50_k2 ## parent 0.5335 5.311    1.599 0.03083   2.058"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l3","dir":"Articles","previous_headings":"","what":"Laboratory Data L3","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L3 FOCUS kinetics report, p. 290.","code":"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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"fit-multiple-models","dir":"Articles","previous_headings":"Laboratory Data L3","what":"Fit multiple models","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"mkin version 0.9-39 (June 2015), can fit several models one datasets one call function mmkin. datasets passed list, case named list holding L3 dataset prepared .  χ2\\chi^2 error level 21% well plot suggest SFO model fit well. FOMC model performs better, error level χ2\\chi^2 test passes 7%. Fitting four parameter DFOP model reduces χ2\\chi^2 error level considerably.","code":"# 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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"accessing-mmkin-objects","dir":"Articles","previous_headings":"Laboratory Data L3","what":"Accessing mmkin objects","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"objects returned mmkin arranged like matrix, models row index datasets column index. can extract summary plot e.g. DFOP fit, using square brackets indexing result use summary plot functions working mkinfit objects.  , look model plot, confidence intervals parameters correlation matrix suggest parameter estimates reliable, DFOP model can used best-fit model based χ2\\chi^2 error level criterion laboratory data L3. also example standard t-test parameter g_ilr misleading, tests significant difference zero. case, zero appears correct value parameter, confidence interval backtransformed parameter g quite narrow.","code":"summary(mm.L3[[\"DFOP\", 1]]) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:27 2025  ##  ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * ##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) ##            * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 376 model solutions performed in 0.024 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 97.80  state ## k1        0.10 deparm ## k2        0.01 deparm ## g         0.50 deparm ##  ## Starting values for the transformed parameters actually optimised: ##              value lower upper ## parent_0 97.800000  -Inf   Inf ## log_k1   -2.302585  -Inf   Inf ## log_k2   -4.605170  -Inf   Inf ## g_qlogis  0.000000  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   32.97732 33.37453 -11.48866 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##          Estimate Std. Error   Lower      Upper ## parent_0  97.7500    1.01900 94.5000 101.000000 ## log_k1    -0.6612    0.10050 -0.9812  -0.341300 ## log_k2    -4.2860    0.04322 -4.4230  -4.148000 ## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142 ## sigma      1.0170    0.25430  0.2079   1.827000 ##  ## Parameter correlation: ##            parent_0     log_k1     log_k2   g_qlogis      sigma ## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.696e-08 ## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07 ## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06 ## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.930e-07 ## sigma    -9.696e-08  7.148e-07  1.022e-06 -7.930e-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 97.75000  95.960 1.248e-06 94.50000 101.00000 ## k1        0.51620   9.947 1.081e-03  0.37490   0.71090 ## k2        0.01376  23.140 8.840e-05  0.01199   0.01579 ## g         0.45660  34.920 2.581e-05  0.41540   0.49850 ## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   2.225       4  4 ## parent     2.225       4  4 ##  ## Estimated disappearance times: ##         DT50 DT90 DT50back DT50_k1 DT50_k2 ## parent 7.464  123    37.03   1.343   50.37 ##  ## Data: ##  time variable observed predicted residual ##     0   parent     97.8     97.75  0.05396 ##     3   parent     60.0     60.45 -0.44933 ##     7   parent     51.0     49.44  1.56338 ##    14   parent     43.0     43.84 -0.83632 ##    30   parent     35.0     35.15 -0.14707 ##    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)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l4","dir":"Articles","previous_headings":"","what":"Laboratory Data L4","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L4 FOCUS kinetics report, p. 293: Fits SFO FOMC models, plots summaries produced :  χ2\\chi^2 error level 3.3% well plot suggest SFO model fits well. error level χ2\\chi^2 test passes slightly lower FOMC model. However, difference appears negligible.","code":"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) # 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),                quiet = TRUE) plot(mm.L4) summary(mm.L4[[\"SFO\", 1]], data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:28 2025  ##  ## Equations: ## d_parent/dt = - k_parent * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 142 model solutions performed in 0.01 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   0.1 deparm ##  ## Starting values for the transformed parameters actually optimised: ##                  value lower upper ## parent_0     96.600000  -Inf   Inf ## log_k_parent -2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   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   -5.030    0.07059 -5.211  -4.848 ## sigma           3.162    0.79050  1.130   5.194 ##  ## Parameter correlation: ##               parent_0 log_k_parent     sigma ## parent_0     1.000e+00    5.938e-01 3.430e-07 ## log_k_parent 5.938e-01    1.000e+00 5.885e-07 ## sigma        3.430e-07    5.885e-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  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 ## All data   3.287       2  6 ## parent     3.287       2  6 ##  ## Estimated disappearance times: ##        DT50 DT90 ## parent  106  352 summary(mm.L4[[\"FOMC\", 1]], data = FALSE) ## mkin version used for fitting:    1.2.10  ## R version used for fitting:       4.5.0  ## Date of fit:     Mon May 12 21:54:27 2025  ## Date of summary: Mon May 12 21:54:28 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 224 model solutions performed in 0.014 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0  96.6  state ## alpha      1.0 deparm ## beta      10.0 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  96.600000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   40.37255 40.69032 -16.18628 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error   Lower    Upper ## parent_0   99.1400     1.2670 95.6300 102.7000 ## log_alpha  -0.3506     0.2616 -1.0770   0.3756 ## log_beta    4.1740     0.3938  3.0810   5.2670 ## sigma       1.8300     0.4575  0.5598   3.1000 ##  ## Parameter correlation: ##             parent_0  log_alpha   log_beta      sigma ## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.447e-07 ## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.198e-08 ## log_beta  -5.543e-01  9.889e-01  1.000e+00  4.923e-08 ## sigma     -2.447e-07  2.198e-08  4.923e-08  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  99.1400  78.250 7.993e-08 95.6300 102.700 ## alpha      0.7042   3.823 9.365e-03  0.3407   1.456 ## beta      64.9800   2.540 3.201e-02 21.7800 193.900 ## sigma      1.8300   4.000 8.065e-03  0.5598   3.100 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   2.029       3  5 ## parent     2.029       3  5 ##  ## Estimated disappearance times: ##         DT50 DT90 DT50back ## parent 108.9 1644    494.9"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"abstract","dir":"Articles","previous_headings":"","what":"Abstract","title":"Short introduction to mkin","text":"regulatory evaluation chemical substances like plant protection products (pesticides), biocides chemicals, degradation data play important role. evaluation pesticide degradation experiments, detailed guidance developed, based nonlinear optimisation. R add-package mkin implements fitting models recommended guidance within R calculates statistical measures data series within one compartments, parent metabolites.","code":"library(\"mkin\", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                          M1 = mkinsub(\"SFO\", \"M2\"),                          M2 = mkinsub(\"SFO\"),                          use_of_ff = \"max\", quiet = TRUE)   # Produce model predictions using some arbitrary parameters 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),   sampling_times)  # Generate a dataset by adding normally distributed errors with # standard deviation 3, for two replicates at each sampling time d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2,                              sdfunc = function(x) 3,                              n = 1, seed = 123456789 )  # Fit the model to the dataset 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\"))"},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"Short introduction to mkin","text":"mkin package (J. Ranke 2021) implements approach degradation kinetics recommended kinetics report provided FOrum Co-ordination pesticide fate models USe (FOCUS Work Group Degradation Kinetics 2006, 2014). covers data series describing decline one compound, data series transformation products (commonly termed metabolites) data series one compartment. possible include back reactions. Therefore, equilibrium reactions equilibrium partitioning can specified, although often leads overparameterisation model. first mkin code published 2010, commonly used tools fitting complex kinetic degradation models experimental data KinGUI (Schäfer et al. 2007), MATLAB based tool graphical user interface specifically tailored task included output proposed FOCUS Kinetics Workgroup, ModelMaker, general purpose compartment based tool providing infrastructure fitting dynamic simulation models based differential equations data. ‘mkin’ code first uploaded BerliOS development platform. taken , version control history imported R-Forge site (see e.g. initial commit 11 May 2010), code still updated. time, R package FME (Flexible Modelling Environment) (Soetaert Petzoldt 2010) already available, provided good basis developing package specifically tailored task. remaining challenge make easy possible users (including author vignette) specify system differential equations include output requested FOCUS guidance, χ2\\chi^2 error level defined guidance. Also, mkin introduced using analytical solutions parent kinetics improved optimization speed. Later, Eigenvalue based solutions introduced mkin case linear differential equations (.e. FOMC DFOP models used parent compound), greatly improving optimization speed cases. , become somehow obsolete, use compiled code described gives even faster execution times. possibility specify back-reactions biphasic model (SFORB) metabolites present mkin beginning.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"derived-software-tools","dir":"Articles","previous_headings":"Background","what":"Derived software tools","title":"Short introduction to mkin","text":"Soon publication mkin, two derived tools published, namely KinGUII (developed Bayer Crop Science) CAKE (commissioned Tessella Syngenta), added graphical user interface (GUI), added fitting iteratively reweighted least squares (IRLS) characterisation likely parameter distributions Markov Chain Monte Carlo (MCMC) sampling. CAKE focuses smooth use experience, sacrificing flexibility model definition, originally allowing two primary metabolites parallel. current version 3.4 CAKE released May 2020 uses scheme six metabolites flexible arrangement supports biphasic modelling metabolites, support back-reactions (non-instantaneous equilibria). KinGUI offers even flexible widget specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) supported early , 2014, simple first-order models specified transformation products. Starting KinGUII version 2.1, biphasic modelling metabolites also available KinGUII. graphical user interface (GUI) recently brought decent degree maturity browser based GUI named gmkin. Please see documentation page manual information. comparison scope, usability numerical results obtained tools recently published Johannes Ranke, Wöltjen, Meinecke (2018).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"unique-features","dir":"Articles","previous_headings":"","what":"Unique features","title":"Short introduction to mkin","text":"Currently, main unique features available mkin speed increase using compiled code compiler present, parallel model fitting multicore machines using mmkin function, estimation parameter confidence intervals based transformed parameters (see ) possibility use two-component error model iteratively reweighted least squares fitting different variances variable introduced Gao et al. (2011) available mkin since version 0.9-22. release 0.9.49.5, IRLS algorithm complemented direct step-wise maximisation likelihood function, makes possible fit variance variable error model also two-component error model inspired error models developed analytical chemistry (Johannes Ranke Meinecke 2019).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"internal-parameter-transformations","dir":"Articles","previous_headings":"","what":"Internal parameter transformations","title":"Short introduction to mkin","text":"rate constants, log transformation used, proposed Bates Watts (1988, 77, 149). Approximate intervals constructed transformed rate constants (compare Bates Watts 1988, 135), .e. logarithms. Confidence intervals rate constants obtained using appropriate backtransformation using exponential function. first version mkin allowing specifying models using formation fractions, home-made reparameterisation used order ensure sum formation fractions exceed unity. method still used current version KinGUII (v2.1 April 2014), modification allows fixing pathway sink zero. CAKE uses penalties objective function order enforce constraint. 2012, alternative reparameterisation formation fractions proposed together René Lehmann (J. Ranke Lehmann 2012), based isometric logratio transformation (ILR). aim improve validity linear approximation objective function parameter estimation procedure well subsequent calculation parameter confidence intervals. current version mkin, logit transformation used parameters bound 0 1, g parameter DFOP model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"confidence-intervals-based-on-transformed-parameters","dir":"Articles","previous_headings":"Internal parameter transformations","what":"Confidence intervals based on transformed parameters","title":"Short introduction to mkin","text":"first attempt providing improved parameter confidence intervals introduced mkin 2013, confidence intervals obtained FME transformed parameters simply backtransformed one one yield asymmetric confidence intervals backtransformed parameters. However, 1:1 relation rate constants model transformed parameters fitted model, parameters obtained isometric logratio transformation calculated set formation fractions quantify paths compounds formed specific parent compound, 1:1 relation exists. Therefore, parameter confidence intervals formation fractions obtained method appear valid case single transformation product, currently logit transformation used formation fraction. confidence intervals obtained backtransformation cases 1:1 relation transformed original parameter exist considered author vignette accurate obtained using re-estimation Hessian matrix backtransformation, implemented FME package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":"parameter-t-test-based-on-untransformed-parameters","dir":"Articles","previous_headings":"Internal parameter transformations","what":"Parameter t-test based on untransformed parameters","title":"Short introduction to mkin","text":"standard output many nonlinear regression software packages includes results test significant difference zero parameters. test also recommended check validity rate constants FOCUS guidance (FOCUS Work Group Degradation Kinetics 2014, 96ff). argued precondition test, .e. normal distribution estimator parameters, fulfilled case nonlinear regression (J. Ranke Lehmann 2015). However, test commonly used industry, consultants national authorities order decide reliability parameter estimates, based FOCUS guidance mentioned . Therefore, results one-sided t-test included summary output mkin. reasonable test significant difference transformed parameters (e.g. log(k)log(k)) zero, t-test calculated based model definition parameter transformation, .e. similar way packages apply internal parameter transformation. note included mkin output, pointing fact t-test based unjustified assumption normal distribution parameter estimators.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS, serial formation two metabolites can fitted mkin package. assembled course work package 1.2 Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) German Environment Agency carried 2022 2023. mkin package used version 1.2.10 currently development. newly introduced functionality used simplification excluding random effects set fits based related set fits reduced model, documentation starting parameters fit, starting parameters saem fits now listed summary. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores()  # We need to start a new cluster after defining a compiled model that is # saved as a DLL to the user directory, therefore we define a function # This is used again after defining the pathway model start_cluster <- function(n_cores) {   if (Sys.info()[\"sysname\"] == \"Windows\") {     ret <- makePSOCKcluster(n_cores)   } else {     ret <- makeForkCluster(n_cores)   }   return(ret) } cl <- start_cluster(n_cores)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"test-data","dir":"Articles > Prebuilt","previous_headings":"Introduction","what":"Test data","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"example data taken final addendum DAR 2014 distributed mkin package. Residue data time step normalisation factors read using function read_spreadsheet mkin package. function also performs time step normalisation. following tables show covariate data 5 datasets read spreadsheet file. Covariate data Dataset Nambsheim Dataset Tama Dataset Gross-Umstadt Dataset Sassafras Dataset Lleida","code":"data_file <- system.file(   \"testdata\", \"cyantraniliprole_soil_efsa_2014.xlsx\",   package = \"mkin\") cyan_ds <- read_spreadsheet(data_file, parent_only = FALSE) pH <- attr(cyan_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(cyan_ds)) {   print(     kable(mkin_long_to_wide(cyan_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE))     cat(\"\\n\\\\clearpage\\n\") }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"parent-only-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Parent only evaluations","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"pathway fits long run times, evaluations parent data performed first, order determine hierarchical parent degradation model random effects degradation model parameters ill-defined. fits converged successfully. almost models, random effect initial concentration parent compound ill-defined. biexponential models DFOP SFORB, random effect one additional parameter ill-defined two-component error model used. Model comparison based AIC BIC indicates two-component error model preferable parent models exception DFOP. lowest AIC BIC values obtained FOMC model, followed SFORB DFOP.","code":"cyan_sep_const <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\"),   cyan_ds, quiet = TRUE, cores = n_cores) cyan_sep_tc <- update(cyan_sep_const, error_model = \"tc\") cyan_saem_full <- mhmkin(list(cyan_sep_const, cyan_sep_tc)) status(cyan_saem_full) |> kable() illparms(cyan_saem_full) |> kable() anova(cyan_saem_full) |> kable(digits = 1) stopCluster(cl)"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"evaluations-with-pathway-established-previously","dir":"Articles > Prebuilt","previous_headings":"Pathway fits","what":"Evaluations with pathway established previously","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"test technical feasibility coupling relevant parent degradation models different transformation pathway models, list mkinmod models set . EU evaluation, parallel formation metabolites JCZ38 J9Z38 secondary formation metabolite JSE76 JCZ38 used. obtain suitable starting values NLHM fits, separate pathway fits performed datasets. separate fits converged successfully. biggest convergence problems seen using HS model constant variance. hierarchical pathway fits, random effects quantified corresponding parent data analyses excluded. code , output illparms function parent fits used argument no_random_effect mhmkin function. possibility introduced mkin version 1.2.2 currently development. status information individual fits shows fits completed successfully. matrix entries Fth FO indicate Fisher Information Matrix inverted fixed effects (theta) random effects (Omega), respectively. affected fits, ill-defined parameters determined using illparms function, relies Fisher Information Matrix. model comparisons suggest pathway fits using DFOP SFORB parent compound provide best fit. two parent model, successful fits shown . Plots fits parent models shown Appendix. DFOP pathway fit two-component error SFORB pathway fit two-component error closer graphical analysis Figures shows residues transformation product JCZ38 soils Tama Nambsheim observed later time points strongly systematically underestimated.","code":"if (!dir.exists(\"cyan_dlls\")) dir.create(\"cyan_dlls\") cyan_path_1 <- list(   sfo_path_1 = mkinmod(     cyan = mkinsub(\"SFO\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"sfo_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   fomc_path_1 = mkinmod(     cyan = mkinsub(\"FOMC\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"fomc_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   dfop_path_1 = mkinmod(     cyan = mkinsub(\"DFOP\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"dfop_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   sforb_path_1 = mkinmod(     cyan = mkinsub(\"SFORB\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"sforb_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE),   hs_path_1 = mkinmod(     cyan = mkinsub(\"HS\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\"), quiet = TRUE,     name = \"hs_path_1\", dll_dir = \"cyan_dlls\", overwrite = TRUE) ) cl_path_1 <- start_cluster(n_cores) f_sep_1_const <- mmkin(   cyan_path_1,   cyan_ds,   error_model = \"const\",   cluster = cl_path_1,   quiet = TRUE) status(f_sep_1_const) |> kable() f_sep_1_tc <- update(f_sep_1_const, error_model = \"tc\") status(f_sep_1_tc) |> kable() f_saem_1 <- mhmkin(list(f_sep_1_const, f_sep_1_tc),   no_random_effect = illparms(cyan_saem_full),   cluster = cl_path_1) status(f_saem_1) |> kable() illparms(f_saem_1) |> kable() anova(f_saem_1[, \"const\"]) |> kable(digits = 1) anova(f_saem_1[1:4, ]) |> kable(digits = 1) plot(f_saem_1[[\"dfop_path_1\", \"tc\"]]) plot(f_saem_1[[\"sforb_path_1\", \"tc\"]]) stopCluster(cl_path_1)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"alternative-pathway-fits","dir":"Articles > Prebuilt","previous_headings":"Pathway fits","what":"Alternative pathway fits","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"improve fit JCZ38, back-reaction JSE76 JCZ38 introduced alternative version transformation pathway, analogy back-reaction K5A78 K5A77. pairs transformation products pairs organic acid corresponding amide (Addendum 2014, p. 109). FOMC provided best fit parent, biexponential models DFOP SFORB provided best initial pathway fits, three parent models used alternative pathway fits. Using constant variance, separate fits converge exception fits Sassafras soil data. Using two-component error model, separate fits converge exception alternative pathway fit DFOP used parent Sassafras dataset. hierarchical fits alternative pathway completed successfully, exception model using FOMC parent compound constant variance error model. biphasic fits (DFOP SFORB parent compound), random effects formation fractions pathways JCZ38 JSE76, reverse pathway JSE76 JCZ38 ill-defined. variants using biexponential models DFOP SFORB parent compound two-component error model give lowest AIC BIC values plotted . Compared original pathway, AIC BIC values indicate large improvement. confirmed plots, show metabolite JCZ38 fitted much better model. FOMC pathway fit two-component error, alternative pathway DFOP pathway fit two-component error, alternative pathway SFORB pathway fit two-component error, alternative pathway","code":"cyan_path_2 <- list(   fomc_path_2 = mkinmod(     cyan = mkinsub(\"FOMC\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\", \"JCZ38\"),     name = \"fomc_path_2\", quiet = TRUE,     dll_dir = \"cyan_dlls\",     overwrite = TRUE   ),   dfop_path_2 = mkinmod(     cyan = mkinsub(\"DFOP\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\", \"JCZ38\"),     name = \"dfop_path_2\", quiet = TRUE,     dll_dir = \"cyan_dlls\",     overwrite = TRUE   ),   sforb_path_2 = mkinmod(     cyan = mkinsub(\"SFORB\", c(\"JCZ38\", \"J9Z38\")),     JCZ38 = mkinsub(\"SFO\", \"JSE76\"),     J9Z38 = mkinsub(\"SFO\"),     JSE76 = mkinsub(\"SFO\", \"JCZ38\"),     name = \"sforb_path_2\", quiet = TRUE,     dll_dir = \"cyan_dlls\",     overwrite = TRUE   ) )  cl_path_2 <- start_cluster(n_cores) f_sep_2_const <- mmkin(   cyan_path_2,   cyan_ds,   error_model = \"const\",   cluster = cl_path_2,   quiet = TRUE)  status(f_sep_2_const) |> kable() f_sep_2_tc <- update(f_sep_2_const, error_model = \"tc\") status(f_sep_2_tc) |> kable() f_saem_2 <- mhmkin(list(f_sep_2_const, f_sep_2_tc),   no_random_effect = illparms(cyan_saem_full[2:4, ]),   cluster = cl_path_2) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable() anova(f_saem_2[, \"tc\"]) |> kable(digits = 1) anova(f_saem_2[2:3,]) |> kable(digits = 1) plot(f_saem_2[[\"fomc_path_2\", \"tc\"]]) plot(f_saem_2[[\"dfop_path_2\", \"tc\"]]) plot(f_saem_2[[\"sforb_path_2\", \"tc\"]])"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"refinement-of-alternative-pathway-fits","dir":"Articles > Prebuilt","previous_headings":"Pathway fits","what":"Refinement of alternative pathway fits","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"ill-defined random effects identified parent fits pathway fits, excluded final evaluations . purpose, list character vectors created can indexed row column indices, contains degradation parameter names random effects excluded hierarchical fits contained f_saem_2. exception FOMC pathway fit constant variance, updated fits completed successfully. However, Fisher Information Matrix fixed effects (Fth) inverted, confidence intervals optimised parameters available. AIC BIC values best fit (DFOP pathway fit two-component error) lower previous fits alternative pathway, practical value refined evaluations limited confidence intervals obtained.","code":"no_ranef <- matrix(list(), nrow = 3, ncol = 2, dimnames = dimnames(f_saem_2)) no_ranef[[\"fomc_path_2\", \"const\"]] <- c(\"log_beta\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"fomc_path_2\", \"tc\"]] <- c(\"cyan_0\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"dfop_path_2\", \"const\"]] <- c(\"cyan_0\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"dfop_path_2\", \"tc\"]] <- c(\"cyan_0\", \"log_k1\", \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"sforb_path_2\", \"const\"]] <- c(\"cyan_free_0\",   \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") no_ranef[[\"sforb_path_2\", \"tc\"]] <- c(\"cyan_free_0\", \"log_k_cyan_free_bound\",   \"f_JCZ38_qlogis\", \"f_JSE76_qlogis\") clusterExport(cl_path_2, \"no_ranef\")  f_saem_3 <- update(f_saem_2,   no_random_effect = no_ranef,   cluster = cl_path_2) status(f_saem_3) |> kable() illparms(f_saem_3) |> kable() anova(f_saem_3[, \"tc\"]) |> kable(digits = 1) anova(f_saem_3[2:3,]) |> kable(digits = 1) stopCluster(cl_path_2)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"conclusion","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusion","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"demonstrated relatively complex transformation pathway parallel formation two primary metabolites one secondary metabolite can fitted even data individual datasets quite different partly cover formation phase. run times pathway fits several hours, limiting practical feasibility iterative refinements based ill-defined parameters alternative checks parameter identifiability based multistart runs.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"acknowledgements","dir":"Articles > Prebuilt","previous_headings":"","what":"Acknowledgements","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"helpful comments Janina Wöltjen German Environment Agency gratefully acknowledged.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"plots-of-fits-that-were-not-refined-further","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Plots of fits that were not refined further","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"SFO pathway fit two-component error FOMC pathway fit two-component error HS pathway fit two-component error","code":"plot(f_saem_1[[\"sfo_path_1\", \"tc\"]]) plot(f_saem_1[[\"fomc_path_1\", \"tc\"]]) plot(f_saem_1[[\"sforb_path_1\", \"tc\"]])"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"pathway-1","dir":"Articles > Prebuilt","previous_headings":"Appendix > Hierarchical fit listings","what":"Pathway 1","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"Hierarchical SFO path 1 fit constant variance  Hierarchical SFO path 1 fit two-component error  Hierarchical FOMC path 1 fit constant variance  Hierarchical FOMC path 1 fit two-component error  Hierarchical DFOP path 1 fit constant variance  Hierarchical DFOP path 1 fit two-component error  Hierarchical SFORB path 1 fit constant variance  Hierarchical SFORB path 1 fit two-component error  Hierarchical HS path 1 fit constant variance","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:35:03 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - k_cyan * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 445.08 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0     log_k_cyan    log_k_JCZ38    log_k_J9Z38    log_k_JSE76         95.3304        -3.8459        -3.1305        -5.0678        -5.3196    f_cyan_ilr_1   f_cyan_ilr_2 f_JCZ38_qlogis          0.8158        23.5335        11.8774   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_0          4.797     0.0000       0.000       0.000      0.0000 log_k_cyan      0.000     0.9619       0.000       0.000      0.0000 log_k_JCZ38     0.000     0.0000       2.139       0.000      0.0000 log_k_J9Z38     0.000     0.0000       0.000       1.639      0.0000 log_k_JSE76     0.000     0.0000       0.000       0.000      0.7894 f_cyan_ilr_1    0.000     0.0000       0.000       0.000      0.0000 f_cyan_ilr_2    0.000     0.0000       0.000       0.000      0.0000 f_JCZ38_qlogis  0.000     0.0000       0.000       0.000      0.0000                f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_0               0.0000        0.000           0.00 log_k_cyan           0.0000        0.000           0.00 log_k_JCZ38          0.0000        0.000           0.00 log_k_J9Z38          0.0000        0.000           0.00 log_k_JSE76          0.0000        0.000           0.00 f_cyan_ilr_1         0.7714        0.000           0.00 f_cyan_ilr_2         0.0000        9.247           0.00 f_JCZ38_qlogis       0.0000        0.000          16.61  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2693 2687  -1331  Optimised parameters:                      est.      lower      upper cyan_0            95.1279  9.354e+01  9.671e+01 log_k_cyan        -3.8527 -4.367e+00 -3.338e+00 log_k_JCZ38       -3.0381 -4.187e+00 -1.889e+00 log_k_J9Z38       -5.0095 -5.623e+00 -4.396e+00 log_k_JSE76       -5.3357 -6.025e+00 -4.646e+00 f_cyan_ilr_1       0.8050  5.174e-01  1.093e+00 f_cyan_ilr_2      12.4820 -1.050e+06  1.051e+06 f_JCZ38_qlogis     1.2912  3.561e-01  2.226e+00 a.1                4.8393         NA         NA SD.log_k_cyan      0.5840         NA         NA SD.log_k_JCZ38     1.2740         NA         NA SD.log_k_J9Z38     0.3172         NA         NA SD.log_k_JSE76     0.5677         NA         NA SD.f_cyan_ilr_1    0.2623         NA         NA SD.f_cyan_ilr_2    1.3724         NA         NA SD.f_JCZ38_qlogis  0.1464         NA         NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_cyan     0.5840    NA    NA SD.log_k_JCZ38    1.2740    NA    NA SD.log_k_J9Z38    0.3172    NA    NA SD.log_k_JSE76    0.5677    NA    NA SD.f_cyan_ilr_1   0.2623    NA    NA SD.f_cyan_ilr_2   1.3724    NA    NA SD.f_JCZ38_qlogis 0.1464    NA    NA  Variance model:      est. lower upper a.1 4.839    NA    NA  Backtransformed parameters:                       est.     lower     upper cyan_0           95.127935 93.542456 96.713413 k_cyan            0.021221  0.012687  0.035497 k_JCZ38           0.047924  0.015189  0.151213 k_J9Z38           0.006674  0.003612  0.012332 k_JSE76           0.004817  0.002417  0.009601 f_cyan_to_JCZ38   0.757402        NA        NA f_cyan_to_J9Z38   0.242597        NA        NA f_JCZ38_to_JSE76  0.784347  0.588098  0.902582  Resulting formation fractions:                    ff cyan_JCZ38  7.574e-01 cyan_J9Z38  2.426e-01 cyan_sink   9.839e-08 JCZ38_JSE76 7.843e-01 JCZ38_sink  2.157e-01  Estimated disappearance times:         DT50   DT90 cyan   32.66 108.50 JCZ38  14.46  48.05 J9Z38 103.86 345.00 JSE76 143.91 478.04 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:34:47 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - k_cyan * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 428.46 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0     log_k_cyan    log_k_JCZ38    log_k_J9Z38    log_k_JSE76         96.0039        -3.8907        -3.1276        -5.0069        -4.9367    f_cyan_ilr_1   f_cyan_ilr_2 f_JCZ38_qlogis          0.7937        22.3422        17.8932   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_0          4.859      0.000        0.00        0.00      0.0000 log_k_cyan      0.000      0.962        0.00        0.00      0.0000 log_k_JCZ38     0.000      0.000        2.04        0.00      0.0000 log_k_J9Z38     0.000      0.000        0.00        1.72      0.0000 log_k_JSE76     0.000      0.000        0.00        0.00      0.9076 f_cyan_ilr_1    0.000      0.000        0.00        0.00      0.0000 f_cyan_ilr_2    0.000      0.000        0.00        0.00      0.0000 f_JCZ38_qlogis  0.000      0.000        0.00        0.00      0.0000                f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_0               0.0000        0.000           0.00 log_k_cyan           0.0000        0.000           0.00 log_k_JCZ38          0.0000        0.000           0.00 log_k_J9Z38          0.0000        0.000           0.00 log_k_JSE76          0.0000        0.000           0.00 f_cyan_ilr_1         0.7598        0.000           0.00 f_cyan_ilr_2         0.0000        8.939           0.00 f_JCZ38_qlogis       0.0000        0.000          14.49  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2658 2651  -1312  Optimised parameters:                       est. lower upper cyan_0            94.81681    NA    NA log_k_cyan        -3.91558    NA    NA log_k_JCZ38       -3.12715    NA    NA log_k_J9Z38       -5.04840    NA    NA log_k_JSE76       -5.10443    NA    NA f_cyan_ilr_1       0.80760    NA    NA f_cyan_ilr_2      48.66960    NA    NA f_JCZ38_qlogis     3.03397    NA    NA a.1                3.93879    NA    NA b.1                0.08057    NA    NA SD.log_k_cyan      0.58921    NA    NA SD.log_k_JCZ38     1.29813    NA    NA SD.log_k_J9Z38     0.68372    NA    NA SD.log_k_JSE76     0.35128    NA    NA SD.f_cyan_ilr_1    0.38352    NA    NA SD.f_cyan_ilr_2    4.98884    NA    NA SD.f_JCZ38_qlogis  1.75636    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_cyan     0.5892    NA    NA SD.log_k_JCZ38    1.2981    NA    NA SD.log_k_J9Z38    0.6837    NA    NA SD.log_k_JSE76    0.3513    NA    NA SD.f_cyan_ilr_1   0.3835    NA    NA SD.f_cyan_ilr_2   4.9888    NA    NA SD.f_JCZ38_qlogis 1.7564    NA    NA  Variance model:        est. lower upper a.1 3.93879    NA    NA b.1 0.08057    NA    NA  Backtransformed parameters:                      est. lower upper cyan_0           94.81681    NA    NA k_cyan            0.01993    NA    NA k_JCZ38           0.04384    NA    NA k_J9Z38           0.00642    NA    NA k_JSE76           0.00607    NA    NA f_cyan_to_JCZ38   0.75807    NA    NA f_cyan_to_J9Z38   0.24193    NA    NA f_JCZ38_to_JSE76  0.95409    NA    NA  Resulting formation fractions:                  ff cyan_JCZ38  0.75807 cyan_J9Z38  0.24193 cyan_sink   0.00000 JCZ38_JSE76 0.95409 JCZ38_sink  0.04591  Estimated disappearance times:         DT50   DT90 cyan   34.78 115.54 JCZ38  15.81  52.52 J9Z38 107.97 358.68 JSE76 114.20 379.35 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:35:21 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 462.739 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.2314        -3.3680        -5.1108        -5.9416         0.7144    f_cyan_ilr_2 f_JCZ38_qlogis      log_alpha       log_beta          7.0229        14.9234        -0.1791         2.9811   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.416       0.000         0.0       0.000       0.0000 log_k_JCZ38     0.000       2.439         0.0       0.000       0.0000 log_k_J9Z38     0.000       0.000         1.7       0.000       0.0000 log_k_JSE76     0.000       0.000         0.0       1.856       0.0000 f_cyan_ilr_1    0.000       0.000         0.0       0.000       0.7164 f_cyan_ilr_2    0.000       0.000         0.0       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000         0.0       0.000       0.0000 log_alpha       0.000       0.000         0.0       0.000       0.0000 log_beta        0.000       0.000         0.0       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta cyan_0                 0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          11.57           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          18.81    0.0000   0.0000 log_alpha              0.00           0.00    0.4144   0.0000 log_beta               0.00           0.00    0.0000   0.5077  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2428 2421  -1196  Optimised parameters:                       est.    lower    upper cyan_0            101.1664 98.51265 103.8202 log_k_JCZ38        -3.3883 -4.78250  -1.9941 log_k_J9Z38        -5.3087 -5.91564  -4.7017 log_k_JSE76        -6.1313 -7.30061  -4.9619 f_cyan_ilr_1        0.7456  0.43782   1.0534 f_cyan_ilr_2        0.8181  0.24956   1.3866 f_JCZ38_qlogis      2.0467  0.61165   3.4817 log_alpha          -0.2391 -0.62806   0.1499 log_beta            2.8739  2.67664   3.0711 a.1                 3.4160  3.17960   3.6525 SD.cyan_0           2.4355  0.40399   4.4671 SD.log_k_JCZ38      1.5654  0.57311   2.5576 SD.log_k_J9Z38      0.4645 -0.06533   0.9943 SD.log_k_JSE76      0.9841  0.10738   1.8609 SD.f_cyan_ilr_1     0.3285  0.10546   0.5515 SD.f_cyan_ilr_2     0.2276 -0.38711   0.8424 SD.f_JCZ38_qlogis   0.8340 -0.20970   1.8777 SD.log_alpha        0.4250  0.16017   0.6898  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_lph log_k_JCZ38    -0.0159                                                         log_k_J9Z38    -0.0546  0.0080                                                 log_k_JSE76    -0.0337  0.0016  0.0074                                         f_cyan_ilr_1   -0.0095  0.0194 -0.1573  0.0003                                 f_cyan_ilr_2   -0.2733  0.0799  0.3059  0.0263  0.0125                         f_JCZ38_qlogis  0.0755 -0.0783 -0.0516  0.1222 -0.1155 -0.5231                 log_alpha      -0.0567  0.0120  0.0351  0.0189  0.0040  0.0829 -0.0502         log_beta       -0.2980  0.0461  0.1382  0.0758  0.0209  0.4079 -0.2053  0.2759  Random effects:                     est.    lower  upper SD.cyan_0         2.4355  0.40399 4.4671 SD.log_k_JCZ38    1.5654  0.57311 2.5576 SD.log_k_J9Z38    0.4645 -0.06533 0.9943 SD.log_k_JSE76    0.9841  0.10738 1.8609 SD.f_cyan_ilr_1   0.3285  0.10546 0.5515 SD.f_cyan_ilr_2   0.2276 -0.38711 0.8424 SD.f_JCZ38_qlogis 0.8340 -0.20970 1.8777 SD.log_alpha      0.4250  0.16017 0.6898  Variance model:      est. lower upper a.1 3.416  3.18 3.652  Backtransformed parameters:                       est.     lower     upper cyan_0           1.012e+02 9.851e+01 103.82023 k_JCZ38          3.377e-02 8.375e-03   0.13614 k_J9Z38          4.948e-03 2.697e-03   0.00908 k_JSE76          2.174e-03 6.751e-04   0.00700 f_cyan_to_JCZ38  6.389e-01        NA        NA f_cyan_to_J9Z38  2.226e-01        NA        NA f_JCZ38_to_JSE76 8.856e-01 6.483e-01   0.97016 alpha            7.873e-01 5.336e-01   1.16166 beta             1.771e+01 1.454e+01  21.56509  Resulting formation fractions:                 ff cyan_JCZ38  0.6389 cyan_J9Z38  0.2226 cyan_sink   0.1385 JCZ38_JSE76 0.8856 JCZ38_sink  0.1144  Estimated disappearance times:         DT50    DT90 DT50back cyan   25.00  312.06    93.94 JCZ38  20.53   68.19       NA J9Z38 140.07  465.32       NA JSE76 318.86 1059.22       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:35:31 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 472.067 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1       101.13294       -3.32499       -5.09097       -5.93566        0.71359    f_cyan_ilr_2 f_JCZ38_qlogis      log_alpha       log_beta        10.30315       14.62272       -0.09633        3.10634   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.649       0.000        0.00        0.00       0.0000 log_k_JCZ38     0.000       2.319        0.00        0.00       0.0000 log_k_J9Z38     0.000       0.000        1.73        0.00       0.0000 log_k_JSE76     0.000       0.000        0.00        1.86       0.0000 f_cyan_ilr_1    0.000       0.000        0.00        0.00       0.7183 f_cyan_ilr_2    0.000       0.000        0.00        0.00       0.0000 f_JCZ38_qlogis  0.000       0.000        0.00        0.00       0.0000 log_alpha       0.000       0.000        0.00        0.00       0.0000 log_beta        0.000       0.000        0.00        0.00       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta cyan_0                 0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          12.85           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          18.54    0.0000   0.0000 log_alpha              0.00           0.00    0.3142   0.0000 log_beta               0.00           0.00    0.0000   0.7333  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2424 2416  -1193  Optimised parameters:                        est. lower upper cyan_0            100.65667    NA    NA log_k_JCZ38        -3.45782    NA    NA log_k_J9Z38        -5.23476    NA    NA log_k_JSE76        -5.71827    NA    NA f_cyan_ilr_1        0.68389    NA    NA f_cyan_ilr_2        0.61027    NA    NA f_JCZ38_qlogis    116.27482    NA    NA log_alpha          -0.14484    NA    NA log_beta            3.03220    NA    NA a.1                 3.11051    NA    NA b.1                 0.04508    NA    NA SD.log_k_JCZ38      1.39961    NA    NA SD.log_k_J9Z38      0.57920    NA    NA SD.log_k_JSE76      0.68364    NA    NA SD.f_cyan_ilr_1     0.31477    NA    NA SD.f_cyan_ilr_2     0.37716    NA    NA SD.f_JCZ38_qlogis   5.52695    NA    NA SD.log_alpha        0.22823    NA    NA SD.log_beta         0.39161    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.3996    NA    NA SD.log_k_J9Z38    0.5792    NA    NA SD.log_k_JSE76    0.6836    NA    NA SD.f_cyan_ilr_1   0.3148    NA    NA SD.f_cyan_ilr_2   0.3772    NA    NA SD.f_JCZ38_qlogis 5.5270    NA    NA SD.log_alpha      0.2282    NA    NA SD.log_beta       0.3916    NA    NA  Variance model:        est. lower upper a.1 3.11051    NA    NA b.1 0.04508    NA    NA  Backtransformed parameters:                       est. lower upper cyan_0           1.007e+02    NA    NA k_JCZ38          3.150e-02    NA    NA k_J9Z38          5.328e-03    NA    NA k_JSE76          3.285e-03    NA    NA f_cyan_to_JCZ38  5.980e-01    NA    NA f_cyan_to_J9Z38  2.273e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA alpha            8.652e-01    NA    NA beta             2.074e+01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.5980 cyan_J9Z38  0.2273 cyan_sink   0.1746 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000  Estimated disappearance times:         DT50  DT90 DT50back cyan   25.48 276.2    83.15 JCZ38  22.01  73.1       NA J9Z38 130.09 432.2       NA JSE76 210.98 700.9       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:36:02 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 503.671 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.0643        -3.4008        -5.0024        -5.8612         0.6855    f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2       g_qlogis          1.2366        13.6901        -1.8641        -4.5063        -0.6468   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          4.466       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       2.382       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.595       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       1.245       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6852 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 log_k1          0.000       0.000       0.000       0.000       0.0000 log_k2          0.000       0.000       0.000       0.000       0.0000 g_qlogis        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis cyan_0                 0.00           0.00 0.0000 0.0000    0.000 log_k_JCZ38            0.00           0.00 0.0000 0.0000    0.000 log_k_J9Z38            0.00           0.00 0.0000 0.0000    0.000 log_k_JSE76            0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_1           0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_2           1.28           0.00 0.0000 0.0000    0.000 f_JCZ38_qlogis         0.00          16.08 0.0000 0.0000    0.000 log_k1                 0.00           0.00 0.9866 0.0000    0.000 log_k2                 0.00           0.00 0.0000 0.5953    0.000 g_qlogis               0.00           0.00 0.0000 0.0000    1.583  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2403 2395  -1182  Optimised parameters:                       est. lower upper cyan_0            102.5565    NA    NA log_k_JCZ38        -3.4729    NA    NA log_k_J9Z38        -5.1533    NA    NA log_k_JSE76        -5.6669    NA    NA f_cyan_ilr_1        0.6665    NA    NA f_cyan_ilr_2        0.5191    NA    NA f_JCZ38_qlogis     37.0113    NA    NA log_k1             -1.8497    NA    NA log_k2             -4.4931    NA    NA g_qlogis           -0.6383    NA    NA a.1                 3.2397    NA    NA SD.log_k_JCZ38      1.4286    NA    NA SD.log_k_J9Z38      0.5312    NA    NA SD.log_k_JSE76      0.6627    NA    NA SD.f_cyan_ilr_1     0.3013    NA    NA SD.f_cyan_ilr_2     0.2980    NA    NA SD.f_JCZ38_qlogis   0.1637    NA    NA SD.log_k1           0.5069    NA    NA SD.log_k2           0.3828    NA    NA SD.g_qlogis         0.8641    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.4286    NA    NA SD.log_k_J9Z38    0.5312    NA    NA SD.log_k_JSE76    0.6627    NA    NA SD.f_cyan_ilr_1   0.3013    NA    NA SD.f_cyan_ilr_2   0.2980    NA    NA SD.f_JCZ38_qlogis 0.1637    NA    NA SD.log_k1         0.5069    NA    NA SD.log_k2         0.3828    NA    NA SD.g_qlogis       0.8641    NA    NA  Variance model:     est. lower upper a.1 3.24    NA    NA  Backtransformed parameters:                       est. lower upper cyan_0           1.026e+02    NA    NA k_JCZ38          3.103e-02    NA    NA k_J9Z38          5.780e-03    NA    NA k_JSE76          3.459e-03    NA    NA f_cyan_to_JCZ38  5.813e-01    NA    NA f_cyan_to_J9Z38  2.265e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA k1               1.573e-01    NA    NA k2               1.119e-02    NA    NA g                3.456e-01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.5813 cyan_J9Z38  0.2265 cyan_sink   0.1922 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   25.23 167.94    50.55   4.407   61.97 JCZ38  22.34  74.22       NA      NA      NA J9Z38 119.92 398.36       NA      NA      NA JSE76 200.41 665.76       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:38:54 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 674.81 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.3964        -3.3626        -4.9792        -5.8727         0.6814    f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2       g_qlogis          6.8713        13.6901        -1.9222        -4.5035        -0.7172   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.317       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       2.272       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.633       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       1.271       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6839 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 log_k1          0.000       0.000       0.000       0.000       0.0000 log_k2          0.000       0.000       0.000       0.000       0.0000 g_qlogis        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis cyan_0                 0.00           0.00 0.0000 0.0000    0.000 log_k_JCZ38            0.00           0.00 0.0000 0.0000    0.000 log_k_J9Z38            0.00           0.00 0.0000 0.0000    0.000 log_k_JSE76            0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_1           0.00           0.00 0.0000 0.0000    0.000 f_cyan_ilr_2          11.95           0.00 0.0000 0.0000    0.000 f_JCZ38_qlogis         0.00          16.08 0.0000 0.0000    0.000 log_k1                 0.00           0.00 0.9496 0.0000    0.000 log_k2                 0.00           0.00 0.0000 0.5846    0.000 g_qlogis               0.00           0.00 0.0000 0.0000    1.719  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2398 2390  -1179  Optimised parameters:                        est. lower upper cyan_0            100.69709    NA    NA log_k_JCZ38        -3.46669    NA    NA log_k_J9Z38        -5.05076    NA    NA log_k_JSE76        -5.55558    NA    NA f_cyan_ilr_1        0.66045    NA    NA f_cyan_ilr_2        0.84275    NA    NA f_JCZ38_qlogis     64.22404    NA    NA log_k1             -2.17715    NA    NA log_k2             -4.55002    NA    NA g_qlogis           -0.55920    NA    NA a.1                 2.95785    NA    NA b.1                 0.04456    NA    NA SD.log_k_JCZ38      1.39881    NA    NA SD.log_k_J9Z38      0.67788    NA    NA SD.log_k_JSE76      0.52603    NA    NA SD.f_cyan_ilr_1     0.32490    NA    NA SD.f_cyan_ilr_2     0.53923    NA    NA SD.f_JCZ38_qlogis   2.75576    NA    NA SD.log_k2           0.30694    NA    NA SD.g_qlogis         0.83619    NA    NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.3988    NA    NA SD.log_k_J9Z38    0.6779    NA    NA SD.log_k_JSE76    0.5260    NA    NA SD.f_cyan_ilr_1   0.3249    NA    NA SD.f_cyan_ilr_2   0.5392    NA    NA SD.f_JCZ38_qlogis 2.7558    NA    NA SD.log_k2         0.3069    NA    NA SD.g_qlogis       0.8362    NA    NA  Variance model:        est. lower upper a.1 2.95785    NA    NA b.1 0.04456    NA    NA  Backtransformed parameters:                       est. lower upper cyan_0           1.007e+02    NA    NA k_JCZ38          3.122e-02    NA    NA k_J9Z38          6.404e-03    NA    NA k_JSE76          3.866e-03    NA    NA f_cyan_to_JCZ38  6.187e-01    NA    NA f_cyan_to_J9Z38  2.431e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA k1               1.134e-01    NA    NA k2               1.057e-02    NA    NA g                3.637e-01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.6187 cyan_J9Z38  0.2431 cyan_sink   0.1382 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   26.35 175.12    52.72   6.114    65.6 JCZ38  22.20  73.75       NA      NA      NA J9Z38 108.23 359.53       NA      NA      NA JSE76 179.30 595.62       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:36:26 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 527.639 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               102.0643               -2.8987               -2.7077  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.4717               -3.4008               -5.0024            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -5.8613                0.6855                1.2366         f_JCZ38_qlogis                13.7395   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 4.466          0.0000                 0.000 log_k_cyan_free             0.000          0.6158                 0.000 log_k_cyan_free_bound       0.000          0.0000                 1.463 log_k_cyan_bound_free       0.000          0.0000                 0.000 log_k_JCZ38                 0.000          0.0000                 0.000 log_k_J9Z38                 0.000          0.0000                 0.000 log_k_JSE76                 0.000          0.0000                 0.000 f_cyan_ilr_1                0.000          0.0000                 0.000 f_cyan_ilr_2                0.000          0.0000                 0.000 f_JCZ38_qlogis              0.000          0.0000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000       0.000       0.000       0.000 log_k_cyan_free                       0.000       0.000       0.000       0.000 log_k_cyan_free_bound                 0.000       0.000       0.000       0.000 log_k_cyan_bound_free                 1.058       0.000       0.000       0.000 log_k_JCZ38                           0.000       2.382       0.000       0.000 log_k_J9Z38                           0.000       0.000       1.595       0.000 log_k_JSE76                           0.000       0.000       0.000       1.245 f_cyan_ilr_1                          0.000       0.000       0.000       0.000 f_cyan_ilr_2                          0.000       0.000       0.000       0.000 f_JCZ38_qlogis                        0.000       0.000       0.000       0.000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_free_0                 0.0000         0.00           0.00 log_k_cyan_free             0.0000         0.00           0.00 log_k_cyan_free_bound       0.0000         0.00           0.00 log_k_cyan_bound_free       0.0000         0.00           0.00 log_k_JCZ38                 0.0000         0.00           0.00 log_k_J9Z38                 0.0000         0.00           0.00 log_k_JSE76                 0.0000         0.00           0.00 f_cyan_ilr_1                0.6852         0.00           0.00 f_cyan_ilr_2                0.0000         1.28           0.00 f_JCZ38_qlogis              0.0000         0.00          16.13  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2401 2394  -1181  Optimised parameters:                              est. lower upper cyan_free_0              102.8136    NA    NA log_k_cyan_free           -2.7935    NA    NA log_k_cyan_free_bound     -2.5440    NA    NA log_k_cyan_bound_free     -3.4303    NA    NA log_k_JCZ38               -3.5010    NA    NA log_k_J9Z38               -5.1226    NA    NA log_k_JSE76               -5.6314    NA    NA f_cyan_ilr_1               0.6609    NA    NA f_cyan_ilr_2               0.5085    NA    NA f_JCZ38_qlogis            44.0153    NA    NA a.1                        3.2318    NA    NA SD.log_k_cyan_free         0.3211    NA    NA SD.log_k_cyan_free_bound   0.8408    NA    NA SD.log_k_cyan_bound_free   0.5724    NA    NA SD.log_k_JCZ38             1.4925    NA    NA SD.log_k_J9Z38             0.5816    NA    NA SD.log_k_JSE76             0.6037    NA    NA SD.f_cyan_ilr_1            0.3115    NA    NA SD.f_cyan_ilr_2            0.3436    NA    NA SD.f_JCZ38_qlogis          4.8937    NA    NA  Correlation is not available  Random effects:                            est. lower upper SD.log_k_cyan_free       0.3211    NA    NA SD.log_k_cyan_free_bound 0.8408    NA    NA SD.log_k_cyan_bound_free 0.5724    NA    NA SD.log_k_JCZ38           1.4925    NA    NA SD.log_k_J9Z38           0.5816    NA    NA SD.log_k_JSE76           0.6037    NA    NA SD.f_cyan_ilr_1          0.3115    NA    NA SD.f_cyan_ilr_2          0.3436    NA    NA SD.f_JCZ38_qlogis        4.8937    NA    NA  Variance model:      est. lower upper a.1 3.232    NA    NA  Backtransformed parameters:                           est. lower upper cyan_free_0          1.028e+02    NA    NA k_cyan_free          6.120e-02    NA    NA k_cyan_free_bound    7.855e-02    NA    NA k_cyan_bound_free    3.238e-02    NA    NA k_JCZ38              3.017e-02    NA    NA k_J9Z38              5.961e-03    NA    NA k_JSE76              3.584e-03    NA    NA f_cyan_free_to_JCZ38 5.784e-01    NA    NA f_cyan_free_to_J9Z38 2.271e-01    NA    NA f_JCZ38_to_JSE76     1.000e+00    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.15973 0.01241 0.33124   Resulting formation fractions:                     ff cyan_free_JCZ38 0.5784 cyan_free_J9Z38 0.2271 cyan_free_sink  0.1945 cyan_free       1.0000 JCZ38_JSE76     1.0000 JCZ38_sink      0.0000  Estimated disappearance times:         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   24.51 153.18    46.11         4.34        55.87 JCZ38  22.98  76.33       NA           NA           NA J9Z38 116.28 386.29       NA           NA           NA JSE76 193.42 642.53       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:38:31 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 651.67 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               101.3964               -2.9881               -2.7949  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.4376               -3.3626               -4.9792            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -5.8727                0.6814                6.7399         f_JCZ38_qlogis                13.7395   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 5.317          0.0000                 0.000 log_k_cyan_free             0.000          0.7301                 0.000 log_k_cyan_free_bound       0.000          0.0000                 1.384 log_k_cyan_bound_free       0.000          0.0000                 0.000 log_k_JCZ38                 0.000          0.0000                 0.000 log_k_J9Z38                 0.000          0.0000                 0.000 log_k_JSE76                 0.000          0.0000                 0.000 f_cyan_ilr_1                0.000          0.0000                 0.000 f_cyan_ilr_2                0.000          0.0000                 0.000 f_JCZ38_qlogis              0.000          0.0000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000       0.000       0.000       0.000 log_k_cyan_free                       0.000       0.000       0.000       0.000 log_k_cyan_free_bound                 0.000       0.000       0.000       0.000 log_k_cyan_bound_free                 1.109       0.000       0.000       0.000 log_k_JCZ38                           0.000       2.272       0.000       0.000 log_k_J9Z38                           0.000       0.000       1.633       0.000 log_k_JSE76                           0.000       0.000       0.000       1.271 f_cyan_ilr_1                          0.000       0.000       0.000       0.000 f_cyan_ilr_2                          0.000       0.000       0.000       0.000 f_JCZ38_qlogis                        0.000       0.000       0.000       0.000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis cyan_free_0                 0.0000         0.00           0.00 log_k_cyan_free             0.0000         0.00           0.00 log_k_cyan_free_bound       0.0000         0.00           0.00 log_k_cyan_bound_free       0.0000         0.00           0.00 log_k_JCZ38                 0.0000         0.00           0.00 log_k_J9Z38                 0.0000         0.00           0.00 log_k_JSE76                 0.0000         0.00           0.00 f_cyan_ilr_1                0.6838         0.00           0.00 f_cyan_ilr_2                0.0000        11.69           0.00 f_JCZ38_qlogis              0.0000         0.00          16.13  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2400 2392  -1180  Optimised parameters:                               est. lower upper cyan_free_0              100.56004    NA    NA log_k_cyan_free           -3.12657    NA    NA log_k_cyan_free_bound     -3.16825    NA    NA log_k_cyan_bound_free     -3.66003    NA    NA log_k_JCZ38               -3.47278    NA    NA log_k_J9Z38               -5.06823    NA    NA log_k_JSE76               -5.54327    NA    NA f_cyan_ilr_1               0.66631    NA    NA f_cyan_ilr_2               0.82898    NA    NA f_JCZ38_qlogis            38.31115    NA    NA a.1                        2.98352    NA    NA b.1                        0.04388    NA    NA SD.log_k_cyan_free         0.49145    NA    NA SD.log_k_cyan_bound_free   0.27347    NA    NA SD.log_k_JCZ38             1.41193    NA    NA SD.log_k_J9Z38             0.66073    NA    NA SD.log_k_JSE76             0.55885    NA    NA SD.f_cyan_ilr_1            0.33020    NA    NA SD.f_cyan_ilr_2            0.51367    NA    NA SD.f_JCZ38_qlogis          5.52122    NA    NA  Correlation is not available  Random effects:                            est. lower upper SD.log_k_cyan_free       0.4914    NA    NA SD.log_k_cyan_bound_free 0.2735    NA    NA SD.log_k_JCZ38           1.4119    NA    NA SD.log_k_J9Z38           0.6607    NA    NA SD.log_k_JSE76           0.5589    NA    NA SD.f_cyan_ilr_1          0.3302    NA    NA SD.f_cyan_ilr_2          0.5137    NA    NA SD.f_JCZ38_qlogis        5.5212    NA    NA  Variance model:        est. lower upper a.1 2.98352    NA    NA b.1 0.04388    NA    NA  Backtransformed parameters:                           est. lower upper cyan_free_0          1.006e+02    NA    NA k_cyan_free          4.387e-02    NA    NA k_cyan_free_bound    4.208e-02    NA    NA k_cyan_bound_free    2.573e-02    NA    NA k_JCZ38              3.103e-02    NA    NA k_J9Z38              6.294e-03    NA    NA k_JSE76              3.914e-03    NA    NA f_cyan_free_to_JCZ38 6.188e-01    NA    NA f_cyan_free_to_J9Z38 2.412e-01    NA    NA f_JCZ38_to_JSE76     1.000e+00    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.10044 0.01124 0.36580   Resulting formation fractions:                     ff cyan_free_JCZ38 0.6188 cyan_free_J9Z38 0.2412 cyan_free_sink  0.1400 cyan_free       1.0000 JCZ38_JSE76     1.0000 JCZ38_sink      0.0000  Estimated disappearance times:         DT50  DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   26.05 164.4    49.48        6.901        61.67 JCZ38  22.34  74.2       NA           NA           NA J9Z38 110.14 365.9       NA           NA           NA JSE76 177.11 588.3       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:36:13 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan -            k_JCZ38 * JCZ38 d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan -            k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 514.353 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.8845        -3.4495        -4.9355        -5.6040         0.6468    f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2         log_tb          1.2396         9.7220        -2.9079        -4.1810         1.7813   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.406        0.00        0.00       0.000       0.0000 log_k_JCZ38     0.000        2.33        0.00       0.000       0.0000 log_k_J9Z38     0.000        0.00        1.59       0.000       0.0000 log_k_JSE76     0.000        0.00        0.00       1.013       0.0000 f_cyan_ilr_1    0.000        0.00        0.00       0.000       0.6367 f_cyan_ilr_2    0.000        0.00        0.00       0.000       0.0000 f_JCZ38_qlogis  0.000        0.00        0.00       0.000       0.0000 log_k1          0.000        0.00        0.00       0.000       0.0000 log_k2          0.000        0.00        0.00       0.000       0.0000 log_tb          0.000        0.00        0.00       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb cyan_0                0.000           0.00 0.0000 0.0000 0.0000 log_k_JCZ38           0.000           0.00 0.0000 0.0000 0.0000 log_k_J9Z38           0.000           0.00 0.0000 0.0000 0.0000 log_k_JSE76           0.000           0.00 0.0000 0.0000 0.0000 f_cyan_ilr_1          0.000           0.00 0.0000 0.0000 0.0000 f_cyan_ilr_2          2.038           0.00 0.0000 0.0000 0.0000 f_JCZ38_qlogis        0.000          10.33 0.0000 0.0000 0.0000 log_k1                0.000           0.00 0.7006 0.0000 0.0000 log_k2                0.000           0.00 0.0000 0.8928 0.0000 log_tb                0.000           0.00 0.0000 0.0000 0.6773  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2427 2419  -1194  Optimised parameters:                       est.      lower      upper cyan_0            101.9660  1.005e+02  1.035e+02 log_k_JCZ38        -3.4698 -4.716e+00 -2.224e+00 log_k_J9Z38        -5.0947 -5.740e+00 -4.450e+00 log_k_JSE76        -5.5977 -6.321e+00 -4.875e+00 f_cyan_ilr_1        0.6595  3.734e-01  9.456e-01 f_cyan_ilr_2        0.5905  1.664e-01  1.015e+00 f_JCZ38_qlogis     25.8627 -4.224e+05  4.225e+05 log_k1             -3.0884 -3.453e+00 -2.723e+00 log_k2             -4.3877 -4.778e+00 -3.998e+00 log_tb              2.3057  1.715e+00  2.896e+00 a.1                 3.3228         NA         NA SD.log_k_JCZ38      1.4071         NA         NA SD.log_k_J9Z38      0.5774         NA         NA SD.log_k_JSE76      0.6214         NA         NA SD.f_cyan_ilr_1     0.3058         NA         NA SD.f_cyan_ilr_2     0.3470         NA         NA SD.f_JCZ38_qlogis   0.0644         NA         NA SD.log_k1           0.3994         NA         NA SD.log_k2           0.4373         NA         NA SD.log_tb           0.6419         NA         NA  Correlation is not available  Random effects:                     est. lower upper SD.log_k_JCZ38    1.4071    NA    NA SD.log_k_J9Z38    0.5774    NA    NA SD.log_k_JSE76    0.6214    NA    NA SD.f_cyan_ilr_1   0.3058    NA    NA SD.f_cyan_ilr_2   0.3470    NA    NA SD.f_JCZ38_qlogis 0.0644    NA    NA SD.log_k1         0.3994    NA    NA SD.log_k2         0.4373    NA    NA SD.log_tb         0.6419    NA    NA  Variance model:      est. lower upper a.1 3.323    NA    NA  Backtransformed parameters:                       est.     lower     upper cyan_0           1.020e+02 1.005e+02 1.035e+02 k_JCZ38          3.112e-02 8.951e-03 1.082e-01 k_J9Z38          6.129e-03 3.216e-03 1.168e-02 k_JSE76          3.706e-03 1.798e-03 7.639e-03 f_cyan_to_JCZ38  5.890e-01        NA        NA f_cyan_to_J9Z38  2.318e-01        NA        NA f_JCZ38_to_JSE76 1.000e+00 0.000e+00 1.000e+00 k1               4.558e-02 3.164e-02 6.565e-02 k2               1.243e-02 8.417e-03 1.835e-02 tb               1.003e+01 5.557e+00 1.811e+01  Resulting formation fractions:                    ff cyan_JCZ38  5.890e-01 cyan_J9Z38  2.318e-01 cyan_sink   1.793e-01 JCZ38_JSE76 1.000e+00 JCZ38_sink  5.861e-12  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   29.02 158.51    47.72   15.21   55.77 JCZ38  22.27  73.98       NA      NA      NA J9Z38 113.09 375.69       NA      NA      NA JSE76 187.01 621.23       NA      NA      NA"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"pathway-2","dir":"Articles > Prebuilt","previous_headings":"Appendix > Hierarchical fit listings","what":"Pathway 2","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"Hierarchical FOMC path 2 fit two-component error  Hierarchical DFOP path 2 fit constant variance  Hierarchical DFOP path 2 fit two-component error  Hierarchical SFORB path 2 fit constant variance  Hierarchical SFORB path 2 fit two-component error","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:47:30 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 505.533 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4477        -1.8631        -5.1087        -2.5114         0.6826    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta          4.7944        15.9616        13.1566        -0.1564         2.9781   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          7.701       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       1.448       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.724       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       3.659       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6356 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000 log_alpha       0.000       0.000       0.000       0.000       0.0000 log_beta        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta cyan_0                 0.00           0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          10.32           0.00           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          12.23           0.00    0.0000   0.0000 f_JSE76_qlogis         0.00           0.00          14.99    0.0000   0.0000 log_alpha              0.00           0.00           0.00    0.3924   0.0000 log_beta               0.00           0.00           0.00    0.0000   0.5639  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2249 2241  -1104  Optimised parameters:                        est.      lower     upper cyan_0            101.55265  9.920e+01  103.9059 log_k_JCZ38        -2.32302 -2.832e+00   -1.8142 log_k_J9Z38        -5.13082 -5.942e+00   -4.3199 log_k_JSE76        -3.01756 -4.262e+00   -1.7736 f_cyan_ilr_1        0.70850  3.657e-01    1.0513 f_cyan_ilr_2        0.95775  2.612e-01    1.6543 f_JCZ38_qlogis      3.86105  9.248e-01    6.7973 f_JSE76_qlogis      7.51583 -1.120e+02  127.0392 log_alpha          -0.15308 -4.508e-01    0.1446 log_beta            2.99165  2.711e+00    3.2720 a.1                 2.04034  1.843e+00    2.2382 b.1                 0.06924  5.749e-02    0.0810 SD.log_k_JCZ38      0.50818  1.390e-01    0.8774 SD.log_k_J9Z38      0.86597  2.652e-01    1.4667 SD.log_k_JSE76      1.38092  4.864e-01    2.2754 SD.f_cyan_ilr_1     0.38204  1.354e-01    0.6286 SD.f_cyan_ilr_2     0.55129  7.198e-02    1.0306 SD.f_JCZ38_qlogis   1.88457  1.711e-02    3.7520 SD.f_JSE76_qlogis   2.64018 -2.450e+03 2454.9447 SD.log_alpha        0.31860  1.047e-01    0.5325 SD.log_beta         0.24195  1.273e-02    0.4712  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 log_k_JCZ38    -0.0235                                                         log_k_J9Z38    -0.0442  0.0047                                                 log_k_JSE76    -0.0023  0.0966  0.0006                                         f_cyan_ilr_1   -0.0032  0.0070 -0.0536 -0.0001                                 f_cyan_ilr_2   -0.5189  0.0452  0.1152  0.0013 -0.0304                         f_JCZ38_qlogis  0.1088 -0.0848 -0.0240  0.0040 -0.0384 -0.2303                 f_JSE76_qlogis -0.0545  0.1315  0.0195  0.0020  0.0252  0.1737 -0.5939         log_alpha      -0.0445  0.0056  0.0261  0.0019 -0.0055  0.0586 -0.0239 -0.0284 log_beta       -0.2388  0.0163  0.0566  0.0040 -0.0078  0.2183 -0.0714 -0.0332                log_lph log_k_JCZ38            log_k_J9Z38            log_k_JSE76            f_cyan_ilr_1           f_cyan_ilr_2           f_JCZ38_qlogis         f_JSE76_qlogis         log_alpha              log_beta        0.2135  Random effects:                     est.      lower     upper SD.log_k_JCZ38    0.5082  1.390e-01    0.8774 SD.log_k_J9Z38    0.8660  2.652e-01    1.4667 SD.log_k_JSE76    1.3809  4.864e-01    2.2754 SD.f_cyan_ilr_1   0.3820  1.354e-01    0.6286 SD.f_cyan_ilr_2   0.5513  7.198e-02    1.0306 SD.f_JCZ38_qlogis 1.8846  1.711e-02    3.7520 SD.f_JSE76_qlogis 2.6402 -2.450e+03 2454.9447 SD.log_alpha      0.3186  1.047e-01    0.5325 SD.log_beta       0.2420  1.273e-02    0.4712  Variance model:        est.   lower upper a.1 2.04034 1.84252 2.238 b.1 0.06924 0.05749 0.081  Backtransformed parameters:                       est.     lower    upper cyan_0           1.016e+02 9.920e+01 103.9059 k_JCZ38          9.798e-02 5.890e-02   0.1630 k_J9Z38          5.912e-03 2.627e-03   0.0133 k_JSE76          4.892e-02 1.410e-02   0.1697 f_cyan_to_JCZ38  6.432e-01        NA       NA f_cyan_to_J9Z38  2.362e-01        NA       NA f_JCZ38_to_JSE76 9.794e-01 7.160e-01   0.9989 f_JSE76_to_JCZ38 9.995e-01 2.268e-49   1.0000 alpha            8.581e-01 6.371e-01   1.1556 beta             1.992e+01 1.505e+01  26.3646  Resulting formation fractions:                    ff cyan_JCZ38  0.6432301 cyan_J9Z38  0.2361657 cyan_sink   0.1206042 JCZ38_JSE76 0.9793879 JCZ38_sink  0.0206121 JSE76_JCZ38 0.9994559 JSE76_sink  0.0005441  Estimated disappearance times:          DT50   DT90 DT50back cyan   24.759 271.61    81.76 JCZ38   7.075  23.50       NA J9Z38 117.249 389.49       NA JSE76  14.169  47.07       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:48:20 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 555.413 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4380        -2.3107        -5.3123        -3.7120         0.6757    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          1.1439        13.1194        12.3492        -1.9317        -4.4557        g_qlogis         -0.5644   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          4.591      0.0000       0.000         0.0       0.0000 log_k_JCZ38     0.000      0.7966       0.000         0.0       0.0000 log_k_J9Z38     0.000      0.0000       1.561         0.0       0.0000 log_k_JSE76     0.000      0.0000       0.000         0.8       0.0000 f_cyan_ilr_1    0.000      0.0000       0.000         0.0       0.6349 f_cyan_ilr_2    0.000      0.0000       0.000         0.0       0.0000 f_JCZ38_qlogis  0.000      0.0000       0.000         0.0       0.0000 f_JSE76_qlogis  0.000      0.0000       0.000         0.0       0.0000 log_k1          0.000      0.0000       0.000         0.0       0.0000 log_k2          0.000      0.0000       0.000         0.0       0.0000 g_qlogis        0.000      0.0000       0.000         0.0       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                0.000           0.00           0.00  0.000 0.0000 log_k_JCZ38           0.000           0.00           0.00  0.000 0.0000 log_k_J9Z38           0.000           0.00           0.00  0.000 0.0000 log_k_JSE76           0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_1          0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_2          1.797           0.00           0.00  0.000 0.0000 f_JCZ38_qlogis        0.000          13.86           0.00  0.000 0.0000 f_JSE76_qlogis        0.000           0.00          13.91  0.000 0.0000 log_k1                0.000           0.00           0.00  1.106 0.0000 log_k2                0.000           0.00           0.00  0.000 0.6141 g_qlogis              0.000           0.00           0.00  0.000 0.0000                g_qlogis cyan_0            0.000 log_k_JCZ38       0.000 log_k_J9Z38       0.000 log_k_JSE76       0.000 f_cyan_ilr_1      0.000 f_cyan_ilr_2      0.000 f_JCZ38_qlogis    0.000 f_JSE76_qlogis    0.000 log_k1            0.000 log_k2            0.000 g_qlogis          1.595  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2288 2280  -1122  Optimised parameters:                       est.      lower      upper cyan_0            102.7204  1.014e+02  1.040e+02 log_k_JCZ38        -2.8925 -4.044e+00 -1.741e+00 log_k_J9Z38        -5.1430 -5.828e+00 -4.457e+00 log_k_JSE76        -3.5577 -4.174e+00 -2.941e+00 f_cyan_ilr_1        0.6929  3.788e-01  1.007e+00 f_cyan_ilr_2        0.6066  5.342e-02  1.160e+00 f_JCZ38_qlogis      9.8071 -2.819e+03  2.838e+03 f_JSE76_qlogis      2.2229  5.684e-01  3.877e+00 log_k1             -1.9339 -2.609e+00 -1.258e+00 log_k2             -4.4709 -4.935e+00 -4.007e+00 g_qlogis           -0.4987 -1.373e+00  3.757e-01 a.1                 2.7368  2.545e+00  2.928e+00 SD.log_k_JCZ38      1.2747  4.577e-01  2.092e+00 SD.log_k_J9Z38      0.6758  1.418e-01  1.210e+00 SD.log_k_JSE76      0.5869  1.169e-01  1.057e+00 SD.f_cyan_ilr_1     0.3392  1.161e-01  5.622e-01 SD.f_cyan_ilr_2     0.4200  8.501e-02  7.550e-01 SD.f_JCZ38_qlogis   0.8511 -1.137e+06  1.137e+06 SD.f_JSE76_qlogis   0.3767 -5.238e-01  1.277e+00 SD.log_k1           0.7475  2.601e-01  1.235e+00 SD.log_k2           0.5179  1.837e-01  8.521e-01 SD.g_qlogis         0.9817  3.553e-01  1.608e+00  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 log_k_JCZ38    -0.0351                                                         log_k_J9Z38    -0.0541  0.0043                                                 log_k_JSE76    -0.0078  0.0900 -0.0014                                         f_cyan_ilr_1   -0.0249  0.0268 -0.0962  0.0000                                 f_cyan_ilr_2   -0.3560  0.0848  0.1545 -0.0022  0.0463                         f_JCZ38_qlogis  0.2005 -0.1226 -0.0347  0.0514 -0.1840 -0.5906                 f_JSE76_qlogis -0.1638  0.1307  0.0266  0.0001  0.1645  0.5181 -0.9297         log_k1          0.0881 -0.0071  0.0005 -0.0070 -0.0064 -0.0346  0.0316 -0.0341 log_k2          0.0238 -0.0003  0.0082 -0.0022 -0.0017 -0.0017 -0.0002 -0.0076 g_qlogis        0.0198 -0.0002 -0.0109  0.0034  0.0017 -0.0176  0.0044  0.0051                log_k1  log_k2  log_k_JCZ38                    log_k_J9Z38                    log_k_JSE76                    f_cyan_ilr_1                   f_cyan_ilr_2                   f_JCZ38_qlogis                 f_JSE76_qlogis                 log_k1                         log_k2          0.0276         g_qlogis       -0.0283 -0.0309  Random effects:                     est.      lower     upper SD.log_k_JCZ38    1.2747  4.577e-01 2.092e+00 SD.log_k_J9Z38    0.6758  1.418e-01 1.210e+00 SD.log_k_JSE76    0.5869  1.169e-01 1.057e+00 SD.f_cyan_ilr_1   0.3392  1.161e-01 5.622e-01 SD.f_cyan_ilr_2   0.4200  8.501e-02 7.550e-01 SD.f_JCZ38_qlogis 0.8511 -1.137e+06 1.137e+06 SD.f_JSE76_qlogis 0.3767 -5.238e-01 1.277e+00 SD.log_k1         0.7475  2.601e-01 1.235e+00 SD.log_k2         0.5179  1.837e-01 8.521e-01 SD.g_qlogis       0.9817  3.553e-01 1.608e+00  Variance model:      est. lower upper a.1 2.737 2.545 2.928  Backtransformed parameters:                       est.     lower     upper cyan_0           102.72037 1.014e+02 104.00464 k_JCZ38            0.05544 1.752e-02   0.17539 k_J9Z38            0.00584 2.942e-03   0.01159 k_JSE76            0.02850 1.539e-02   0.05279 f_cyan_to_JCZ38    0.59995        NA        NA f_cyan_to_J9Z38    0.22519        NA        NA f_JCZ38_to_JSE76   0.99994 0.000e+00   1.00000 f_JSE76_to_JCZ38   0.90229 6.384e-01   0.97971 k1                 0.14459 7.357e-02   0.28414 k2                 0.01144 7.192e-03   0.01819 g                  0.37784 2.021e-01   0.59284  Resulting formation fractions:                    ff cyan_JCZ38  5.999e-01 cyan_J9Z38  2.252e-01 cyan_sink   1.749e-01 JCZ38_JSE76 9.999e-01 JCZ38_sink  5.506e-05 JSE76_JCZ38 9.023e-01 JSE76_sink  9.771e-02  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   21.93 159.83    48.11   4.794    60.6 JCZ38  12.50  41.53       NA      NA      NA J9Z38 118.69 394.27       NA      NA      NA JSE76  24.32  80.78       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:51:02 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 717.675 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.7393        -1.4493        -5.0118        -2.1269         0.6720    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          7.3362        13.4423        13.2659        -2.0061        -4.5527        g_qlogis         -0.5806   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.604        0.00       0.000       0.000       0.0000 log_k_JCZ38     0.000        2.77       0.000       0.000       0.0000 log_k_J9Z38     0.000        0.00       1.662       0.000       0.0000 log_k_JSE76     0.000        0.00       0.000       5.021       0.0000 f_cyan_ilr_1    0.000        0.00       0.000       0.000       0.6519 f_cyan_ilr_2    0.000        0.00       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000        0.00       0.000       0.000       0.0000 f_JSE76_qlogis  0.000        0.00       0.000       0.000       0.0000 log_k1          0.000        0.00       0.000       0.000       0.0000 log_k2          0.000        0.00       0.000       0.000       0.0000 g_qlogis        0.000        0.00       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                 0.00           0.00           0.00 0.0000 0.0000 log_k_JCZ38            0.00           0.00           0.00 0.0000 0.0000 log_k_J9Z38            0.00           0.00           0.00 0.0000 0.0000 log_k_JSE76            0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_1           0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_2          13.37           0.00           0.00 0.0000 0.0000 f_JCZ38_qlogis         0.00          14.21           0.00 0.0000 0.0000 f_JSE76_qlogis         0.00           0.00          14.58 0.0000 0.0000 log_k1                 0.00           0.00           0.00 0.8453 0.0000 log_k2                 0.00           0.00           0.00 0.0000 0.5969 g_qlogis               0.00           0.00           0.00 0.0000 0.0000                g_qlogis cyan_0             0.00 log_k_JCZ38        0.00 log_k_J9Z38        0.00 log_k_JSE76        0.00 f_cyan_ilr_1       0.00 f_cyan_ilr_2       0.00 f_JCZ38_qlogis     0.00 f_JSE76_qlogis     0.00 log_k1             0.00 log_k2             0.00 g_qlogis           1.69  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2234 2226  -1095  Optimised parameters:                        est.     lower     upper cyan_0            101.25496  99.14662 103.36331 log_k_JCZ38        -2.55593  -3.32972  -1.78215 log_k_J9Z38        -5.07103  -5.85423  -4.28783 log_k_JSE76        -3.25468  -4.17577  -2.33360 f_cyan_ilr_1        0.70139   0.35924   1.04355 f_cyan_ilr_2        1.07712   0.17789   1.97636 f_JCZ38_qlogis      3.57483   0.05990   7.08976 f_JSE76_qlogis      4.54884  -7.25628  16.35395 log_k1             -2.38201  -2.51639  -2.24763 log_k2             -4.66741  -4.91865  -4.41617 g_qlogis           -0.28446  -1.14192   0.57300 a.1                 2.05925   1.86481   2.25369 b.1                 0.06172   0.05062   0.07282 SD.log_k_JCZ38      0.81137   0.25296   1.36977 SD.log_k_J9Z38      0.83542   0.25395   1.41689 SD.log_k_JSE76      0.97903   0.30100   1.65707 SD.f_cyan_ilr_1     0.37878   0.13374   0.62382 SD.f_cyan_ilr_2     0.67274   0.10102   1.24446 SD.f_JCZ38_qlogis   1.35327  -0.42359   3.13012 SD.f_JSE76_qlogis   1.43956 -19.14972  22.02884 SD.log_k2           0.25329   0.07521   0.43138 SD.g_qlogis         0.95167   0.35149   1.55184  Correlation:                 cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 log_k_JCZ38    -0.0265                                                         log_k_J9Z38    -0.0392  0.0024                                                 log_k_JSE76     0.0011  0.1220 -0.0016                                         f_cyan_ilr_1   -0.0161  0.0217 -0.0552  0.0034                                 f_cyan_ilr_2   -0.4718  0.0829  0.1102  0.0042  0.0095                         f_JCZ38_qlogis  0.1609 -0.1318 -0.0277  0.0081 -0.1040 -0.4559                 f_JSE76_qlogis -0.1289  0.1494  0.0219  0.0012  0.1004  0.4309 -0.8543         log_k1          0.2618 -0.0739 -0.0167 -0.0148 -0.0444 -0.2768  0.3518 -0.3818 log_k2          0.0603 -0.0217  0.0174 -0.0058 -0.0197 -0.0533  0.0923 -0.1281 g_qlogis        0.0362  0.0115 -0.0111  0.0040  0.0095 -0.0116 -0.0439  0.0651                log_k1  log_k2  log_k_JCZ38                    log_k_J9Z38                    log_k_JSE76                    f_cyan_ilr_1                   f_cyan_ilr_2                   f_JCZ38_qlogis                 f_JSE76_qlogis                 log_k1                         log_k2          0.3269         g_qlogis       -0.1656 -0.0928  Random effects:                     est.     lower   upper SD.log_k_JCZ38    0.8114   0.25296  1.3698 SD.log_k_J9Z38    0.8354   0.25395  1.4169 SD.log_k_JSE76    0.9790   0.30100  1.6571 SD.f_cyan_ilr_1   0.3788   0.13374  0.6238 SD.f_cyan_ilr_2   0.6727   0.10102  1.2445 SD.f_JCZ38_qlogis 1.3533  -0.42359  3.1301 SD.f_JSE76_qlogis 1.4396 -19.14972 22.0288 SD.log_k2         0.2533   0.07521  0.4314 SD.g_qlogis       0.9517   0.35149  1.5518  Variance model:        est.   lower   upper a.1 2.05925 1.86481 2.25369 b.1 0.06172 0.05062 0.07282  Backtransformed parameters:                       est.     lower     upper cyan_0           1.013e+02 9.915e+01 103.36331 k_JCZ38          7.762e-02 3.580e-02   0.16828 k_J9Z38          6.276e-03 2.868e-03   0.01373 k_JSE76          3.859e-02 1.536e-02   0.09695 f_cyan_to_JCZ38  6.520e-01        NA        NA f_cyan_to_J9Z38  2.418e-01        NA        NA f_JCZ38_to_JSE76 9.727e-01 5.150e-01   0.99917 f_JSE76_to_JCZ38 9.895e-01 7.052e-04   1.00000 k1               9.236e-02 8.075e-02   0.10565 k2               9.397e-03 7.309e-03   0.01208 g                4.294e-01 2.420e-01   0.63945  Resulting formation fractions:                  ff cyan_JCZ38  0.65203 cyan_J9Z38  0.24181 cyan_sink   0.10616 JCZ38_JSE76 0.97274 JCZ38_sink  0.02726 JSE76_JCZ38 0.98953 JSE76_sink  0.01047  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   24.26 185.34    55.79   7.504   73.77 JCZ38   8.93  29.66       NA      NA      NA J9Z38 110.45 366.89       NA      NA      NA JSE76  17.96  59.66       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:48:15 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 550.772 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               102.4395               -2.7673               -2.8942  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6201               -2.3107               -5.3123            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.7120                0.6754                1.1448         f_JCZ38_qlogis        f_JSE76_qlogis                14.8408               15.4734   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 4.589          0.0000                  0.00 log_k_cyan_free             0.000          0.4849                  0.00 log_k_cyan_free_bound       0.000          0.0000                  1.62 log_k_cyan_bound_free       0.000          0.0000                  0.00 log_k_JCZ38                 0.000          0.0000                  0.00 log_k_J9Z38                 0.000          0.0000                  0.00 log_k_JSE76                 0.000          0.0000                  0.00 f_cyan_ilr_1                0.000          0.0000                  0.00 f_cyan_ilr_2                0.000          0.0000                  0.00 f_JCZ38_qlogis              0.000          0.0000                  0.00 f_JSE76_qlogis              0.000          0.0000                  0.00                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000         0.0 log_k_cyan_free                       0.000      0.0000       0.000         0.0 log_k_cyan_free_bound                 0.000      0.0000       0.000         0.0 log_k_cyan_bound_free                 1.197      0.0000       0.000         0.0 log_k_JCZ38                           0.000      0.7966       0.000         0.0 log_k_J9Z38                           0.000      0.0000       1.561         0.0 log_k_JSE76                           0.000      0.0000       0.000         0.8 f_cyan_ilr_1                          0.000      0.0000       0.000         0.0 f_cyan_ilr_2                          0.000      0.0000       0.000         0.0 f_JCZ38_qlogis                        0.000      0.0000       0.000         0.0 f_JSE76_qlogis                        0.000      0.0000       0.000         0.0                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000            0.0           0.00 log_k_cyan_free             0.0000        0.000            0.0           0.00 log_k_cyan_free_bound       0.0000        0.000            0.0           0.00 log_k_cyan_bound_free       0.0000        0.000            0.0           0.00 log_k_JCZ38                 0.0000        0.000            0.0           0.00 log_k_J9Z38                 0.0000        0.000            0.0           0.00 log_k_JSE76                 0.0000        0.000            0.0           0.00 f_cyan_ilr_1                0.6349        0.000            0.0           0.00 f_cyan_ilr_2                0.0000        1.797            0.0           0.00 f_JCZ38_qlogis              0.0000        0.000           15.6           0.00 f_JSE76_qlogis              0.0000        0.000            0.0          17.52  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2283 2275  -1120  Optimised parameters:                              est.     lower    upper cyan_free_0              102.6517 101.40815 103.8952 log_k_cyan_free           -2.8729  -3.18649  -2.5593 log_k_cyan_free_bound     -2.7803  -3.60525  -1.9552 log_k_cyan_bound_free     -3.5845  -4.16644  -3.0026 log_k_JCZ38               -2.3411  -2.89698  -1.7852 log_k_J9Z38               -5.2487  -6.01271  -4.4847 log_k_JSE76               -3.0259  -4.28274  -1.7690 f_cyan_ilr_1               0.7289   0.38214   1.0756 f_cyan_ilr_2               0.6891   0.18277   1.1954 f_JCZ38_qlogis             4.2162   0.47015   7.9622 f_JSE76_qlogis             5.8911 -20.19088  31.9730 a.1                        2.7159   2.52587   2.9060 SD.log_k_cyan_free         0.3354   0.10979   0.5610 SD.log_k_cyan_free_bound   0.9061   0.30969   1.5025 SD.log_k_cyan_bound_free   0.6376   0.21229   1.0628 SD.log_k_JCZ38             0.5499   0.14533   0.9545 SD.log_k_J9Z38             0.7457   0.15106   1.3404 SD.log_k_JSE76             1.3822   0.47329   2.2912 SD.f_cyan_ilr_1            0.3820   0.13280   0.6313 SD.f_cyan_ilr_2            0.4317   0.06803   0.7953 SD.f_JCZ38_qlogis          1.8258  -0.25423   3.9059 SD.f_JSE76_qlogis          2.2348 -83.33679  87.8065  Correlation:                        cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 log_k_cyan_free        0.1944                                                 log_k_cyan_free_bound  0.0815  0.0814                                         log_k_cyan_bound_free  0.0106  0.0426  0.0585                                 log_k_JCZ38           -0.0231 -0.0106 -0.0089     -0.0051                     log_k_J9Z38           -0.0457 -0.0108  0.0019      0.0129      0.0032         log_k_JSE76           -0.0054 -0.0024 -0.0017     -0.0005      0.1108  0.0009 f_cyan_ilr_1           0.0051 -0.0005 -0.0035     -0.0056      0.0131 -0.0967 f_cyan_ilr_2          -0.3182 -0.0771 -0.0309     -0.0038      0.0680  0.1643 f_JCZ38_qlogis         0.0834  0.0369  0.0302      0.0172     -0.1145 -0.0204 f_JSE76_qlogis        -0.0553 -0.0365 -0.0441     -0.0414      0.1579  0.0175                       l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_k_cyan_free                                       log_k_cyan_free_bound                                 log_k_cyan_bound_free                                 log_k_JCZ38                                           log_k_J9Z38                                           log_k_JSE76                                           f_cyan_ilr_1          -0.0002                         f_cyan_ilr_2           0.0020 -0.0415                 f_JCZ38_qlogis         0.0052 -0.0665 -0.3437         f_JSE76_qlogis         0.0066  0.0635  0.3491 -0.7487  Random effects:                            est.     lower   upper SD.log_k_cyan_free       0.3354   0.10979  0.5610 SD.log_k_cyan_free_bound 0.9061   0.30969  1.5025 SD.log_k_cyan_bound_free 0.6376   0.21229  1.0628 SD.log_k_JCZ38           0.5499   0.14533  0.9545 SD.log_k_J9Z38           0.7457   0.15106  1.3404 SD.log_k_JSE76           1.3822   0.47329  2.2912 SD.f_cyan_ilr_1          0.3820   0.13280  0.6313 SD.f_cyan_ilr_2          0.4317   0.06803  0.7953 SD.f_JCZ38_qlogis        1.8258  -0.25423  3.9059 SD.f_JSE76_qlogis        2.2348 -83.33679 87.8065  Variance model:      est. lower upper a.1 2.716 2.526 2.906  Backtransformed parameters:                           est.     lower     upper cyan_free_0          1.027e+02 1.014e+02 103.89517 k_cyan_free          5.654e-02 4.132e-02   0.07736 k_cyan_free_bound    6.202e-02 2.718e-02   0.14153 k_cyan_bound_free    2.775e-02 1.551e-02   0.04966 k_JCZ38              9.622e-02 5.519e-02   0.16777 k_J9Z38              5.254e-03 2.447e-03   0.01128 k_JSE76              4.852e-02 1.380e-02   0.17051 f_cyan_free_to_JCZ38 6.197e-01 5.643e-01   0.84429 f_cyan_free_to_J9Z38 2.211e-01 5.643e-01   0.84429 f_JCZ38_to_JSE76     9.855e-01 6.154e-01   0.99965 f_JSE76_to_JCZ38     9.972e-01 1.703e-09   1.00000  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.13466 0.01165 0.36490   Resulting formation fractions:                       ff cyan_free_JCZ38 0.619745 cyan_free_J9Z38 0.221083 cyan_free_sink  0.159172 cyan_free       1.000000 JCZ38_JSE76     0.985460 JCZ38_sink      0.014540 JSE76_JCZ38     0.997244 JSE76_sink      0.002756  Estimated disappearance times:          DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   23.293 158.67    47.77        5.147         59.5 JCZ38   7.203  23.93       NA           NA           NA J9Z38 131.918 438.22       NA           NA           NA JSE76  14.287  47.46       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 20:50:59 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 714.48 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               101.7511               -2.8370               -3.0162  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6600               -2.2988               -5.3129            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.6991                0.6722                4.8596         f_JCZ38_qlogis        f_JSE76_qlogis                13.4678               14.2149   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 5.629           0.000                 0.000 log_k_cyan_free             0.000           0.446                 0.000 log_k_cyan_free_bound       0.000           0.000                 1.449 log_k_cyan_bound_free       0.000           0.000                 0.000 log_k_JCZ38                 0.000           0.000                 0.000 log_k_J9Z38                 0.000           0.000                 0.000 log_k_JSE76                 0.000           0.000                 0.000 f_cyan_ilr_1                0.000           0.000                 0.000 f_cyan_ilr_2                0.000           0.000                 0.000 f_JCZ38_qlogis              0.000           0.000                 0.000 f_JSE76_qlogis              0.000           0.000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000      0.0000 log_k_cyan_free                       0.000      0.0000       0.000      0.0000 log_k_cyan_free_bound                 0.000      0.0000       0.000      0.0000 log_k_cyan_bound_free                 1.213      0.0000       0.000      0.0000 log_k_JCZ38                           0.000      0.7801       0.000      0.0000 log_k_J9Z38                           0.000      0.0000       1.575      0.0000 log_k_JSE76                           0.000      0.0000       0.000      0.8078 f_cyan_ilr_1                          0.000      0.0000       0.000      0.0000 f_cyan_ilr_2                          0.000      0.0000       0.000      0.0000 f_JCZ38_qlogis                        0.000      0.0000       0.000      0.0000 f_JSE76_qlogis                        0.000      0.0000       0.000      0.0000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000           0.00           0.00 log_k_cyan_free             0.0000        0.000           0.00           0.00 log_k_cyan_free_bound       0.0000        0.000           0.00           0.00 log_k_cyan_bound_free       0.0000        0.000           0.00           0.00 log_k_JCZ38                 0.0000        0.000           0.00           0.00 log_k_J9Z38                 0.0000        0.000           0.00           0.00 log_k_JSE76                 0.0000        0.000           0.00           0.00 f_cyan_ilr_1                0.6518        0.000           0.00           0.00 f_cyan_ilr_2                0.0000        9.981           0.00           0.00 f_JCZ38_qlogis              0.0000        0.000          14.26           0.00 f_JSE76_qlogis              0.0000        0.000           0.00          16.17  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2240 2231  -1098  Optimised parameters:                               est.      lower      upper cyan_free_0              100.73014  9.873e+01  1.027e+02 log_k_cyan_free           -3.19634 -3.641e+00 -2.752e+00 log_k_cyan_free_bound     -3.43533 -3.674e+00 -3.197e+00 log_k_cyan_bound_free     -3.83282 -4.163e+00 -3.503e+00 log_k_JCZ38               -2.51065 -3.225e+00 -1.796e+00 log_k_J9Z38               -5.02539 -5.825e+00 -4.226e+00 log_k_JSE76               -3.24777 -4.163e+00 -2.333e+00 f_cyan_ilr_1               0.70640  3.562e-01  1.057e+00 f_cyan_ilr_2               1.42704  3.170e-01  2.537e+00 f_JCZ38_qlogis             2.84779  1.042e+00  4.654e+00 f_JSE76_qlogis             8.63674 -6.407e+02  6.580e+02 a.1                        2.07082  1.877e+00  2.265e+00 b.1                        0.06227  5.098e-02  7.355e-02 SD.log_k_cyan_free         0.49674  1.865e-01  8.069e-01 SD.log_k_cyan_bound_free   0.28537  6.809e-02  5.027e-01 SD.log_k_JCZ38             0.74846  2.305e-01  1.266e+00 SD.log_k_J9Z38             0.86077  2.713e-01  1.450e+00 SD.log_k_JSE76             0.97613  3.030e-01  1.649e+00 SD.f_cyan_ilr_1            0.38994  1.382e-01  6.417e-01 SD.f_cyan_ilr_2            0.82869  3.917e-02  1.618e+00 SD.f_JCZ38_qlogis          1.05000 -2.808e-02  2.128e+00 SD.f_JSE76_qlogis          0.44681 -3.985e+05  3.985e+05  Correlation:                        cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 log_k_cyan_free        0.0936                                                 log_k_cyan_free_bound  0.1302  0.1627                                         log_k_cyan_bound_free  0.0029  0.0525  0.5181                                 log_k_JCZ38           -0.0116 -0.0077 -0.0430     -0.0236                     log_k_J9Z38           -0.0192 -0.0077 -0.0048      0.0229     -0.0005         log_k_JSE76            0.0007 -0.0020 -0.0134     -0.0072      0.1225 -0.0016 f_cyan_ilr_1          -0.0118 -0.0027 -0.0132     -0.0118      0.0127 -0.0505 f_cyan_ilr_2          -0.4643 -0.0762 -0.1245      0.0137      0.0497  0.1003 f_JCZ38_qlogis         0.0710  0.0371  0.1826      0.0925     -0.0869 -0.0130 f_JSE76_qlogis        -0.0367 -0.0270 -0.2274     -0.1865      0.1244  0.0098                       l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_k_cyan_free                                       log_k_cyan_free_bound                                 log_k_cyan_bound_free                                 log_k_JCZ38                                           log_k_J9Z38                                           log_k_JSE76                                           f_cyan_ilr_1           0.0036                         f_cyan_ilr_2           0.0050 -0.0201                 f_JCZ38_qlogis         0.0142 -0.0529 -0.2698         f_JSE76_qlogis         0.0064  0.0345  0.2015 -0.7058  Random effects:                            est.      lower     upper SD.log_k_cyan_free       0.4967  1.865e-01 8.069e-01 SD.log_k_cyan_bound_free 0.2854  6.809e-02 5.027e-01 SD.log_k_JCZ38           0.7485  2.305e-01 1.266e+00 SD.log_k_J9Z38           0.8608  2.713e-01 1.450e+00 SD.log_k_JSE76           0.9761  3.030e-01 1.649e+00 SD.f_cyan_ilr_1          0.3899  1.382e-01 6.417e-01 SD.f_cyan_ilr_2          0.8287  3.917e-02 1.618e+00 SD.f_JCZ38_qlogis        1.0500 -2.808e-02 2.128e+00 SD.f_JSE76_qlogis        0.4468 -3.985e+05 3.985e+05  Variance model:        est.   lower   upper a.1 2.07082 1.87680 2.26483 b.1 0.06227 0.05098 0.07355  Backtransformed parameters:                           est.      lower     upper cyan_free_0          1.007e+02  9.873e+01 102.72898 k_cyan_free          4.091e-02  2.623e-02   0.06382 k_cyan_free_bound    3.221e-02  2.537e-02   0.04090 k_cyan_bound_free    2.165e-02  1.557e-02   0.03011 k_JCZ38              8.122e-02  3.975e-02   0.16594 k_J9Z38              6.569e-03  2.954e-03   0.01461 k_JSE76              3.886e-02  1.556e-02   0.09703 f_cyan_free_to_JCZ38 6.785e-01  6.102e-01   0.97309 f_cyan_free_to_J9Z38 2.498e-01  6.102e-01   0.97309 f_JCZ38_to_JSE76     9.452e-01  7.392e-01   0.99056 f_JSE76_to_JCZ38     9.998e-01 5.580e-279   1.00000  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.08426 0.01051 0.41220   Resulting formation fractions:                        ff cyan_free_JCZ38 0.6784541 cyan_free_J9Z38 0.2498405 cyan_free_sink  0.0717054 cyan_free       1.0000000 JCZ38_JSE76     0.9452043 JCZ38_sink      0.0547957 JSE76_JCZ38     0.9998226 JSE76_sink      0.0001774  Estimated disappearance times:          DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   25.237 168.51    50.73        8.226        65.95 JCZ38   8.535  28.35       NA           NA           NA J9Z38 105.517 350.52       NA           NA           NA JSE76  17.837  59.25       NA           NA           NA"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"pathway-2-refined-fits","dir":"Articles > Prebuilt","previous_headings":"Appendix > Hierarchical fit listings","what":"Pathway 2, refined fits","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"Hierarchical FOMC path 2 fit reduced random effects, two-component error  Hierarchical DFOP path 2 fit reduced random effects, constant variance  Hierarchical DFOP path 2 fit reduced random effects, two-component error  Hierarchical SFORB path 2 fit reduced random effects, constant variance  Hierarchical SFORB path 2 fit reduced random effects, two-component error","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:04:10 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *            cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 786.038 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4477        -1.8631        -5.1087        -2.5114         0.6826    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta          4.7944        15.9616        13.1566        -0.1564         2.9781   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          7.701       0.000       0.000       0.000       0.0000 log_k_JCZ38     0.000       1.448       0.000       0.000       0.0000 log_k_J9Z38     0.000       0.000       1.724       0.000       0.0000 log_k_JSE76     0.000       0.000       0.000       3.659       0.0000 f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6356 f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000 f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000 log_alpha       0.000       0.000       0.000       0.000       0.0000 log_beta        0.000       0.000       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta cyan_0                 0.00           0.00           0.00    0.0000   0.0000 log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000 log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000 log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000 f_cyan_ilr_2          10.32           0.00           0.00    0.0000   0.0000 f_JCZ38_qlogis         0.00          12.23           0.00    0.0000   0.0000 f_JSE76_qlogis         0.00           0.00          14.99    0.0000   0.0000 log_alpha              0.00           0.00           0.00    0.3924   0.0000 log_beta               0.00           0.00           0.00    0.0000   0.5639  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2249 2242  -1106  Optimised parameters:                      est.   lower  upper cyan_0          101.24524      NA     NA log_k_JCZ38      -2.85375      NA     NA log_k_J9Z38      -5.07729      NA     NA log_k_JSE76      -3.53511      NA     NA f_cyan_ilr_1      0.67478      NA     NA f_cyan_ilr_2      0.97152      NA     NA f_JCZ38_qlogis  213.48001      NA     NA f_JSE76_qlogis    2.02040      NA     NA log_alpha        -0.11041      NA     NA log_beta          3.06575      NA     NA a.1               2.05279 1.85495 2.2506 b.1               0.07116 0.05912 0.0832 SD.log_k_JCZ38    1.21713 0.44160 1.9927 SD.log_k_J9Z38    0.88268 0.27541 1.4900 SD.log_k_JSE76    0.59452 0.15005 1.0390 SD.f_cyan_ilr_1   0.35370 0.12409 0.5833 SD.f_cyan_ilr_2   0.78186 0.18547 1.3782 SD.log_alpha      0.27781 0.08168 0.4739 SD.log_beta       0.32608 0.06490 0.5873  Correlation is not available  Random effects:                   est.   lower  upper SD.log_k_JCZ38  1.2171 0.44160 1.9927 SD.log_k_J9Z38  0.8827 0.27541 1.4900 SD.log_k_JSE76  0.5945 0.15005 1.0390 SD.f_cyan_ilr_1 0.3537 0.12409 0.5833 SD.f_cyan_ilr_2 0.7819 0.18547 1.3782 SD.log_alpha    0.2778 0.08168 0.4739 SD.log_beta     0.3261 0.06490 0.5873  Variance model:        est.   lower  upper a.1 2.05279 1.85495 2.2506 b.1 0.07116 0.05912 0.0832  Backtransformed parameters:                       est. lower upper cyan_0           1.012e+02    NA    NA k_JCZ38          5.763e-02    NA    NA k_J9Z38          6.237e-03    NA    NA k_JSE76          2.916e-02    NA    NA f_cyan_to_JCZ38  6.354e-01    NA    NA f_cyan_to_J9Z38  2.447e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA f_JSE76_to_JCZ38 8.829e-01    NA    NA alpha            8.955e-01    NA    NA beta             2.145e+01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.6354 cyan_J9Z38  0.2447 cyan_sink   0.1200 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000 JSE76_JCZ38 0.8829 JSE76_sink  0.1171  Estimated disappearance times:         DT50   DT90 DT50back cyan   25.07 259.21    78.03 JCZ38  12.03  39.96       NA J9Z38 111.14 369.19       NA JSE76  23.77  78.98       NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:05:56 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 892.139 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        102.4380        -2.3107        -5.3123        -3.7120         0.6757    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          1.1439        13.1194        12.3492        -1.9317        -4.4557        g_qlogis         -0.5644   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          4.591      0.0000       0.000         0.0       0.0000 log_k_JCZ38     0.000      0.7966       0.000         0.0       0.0000 log_k_J9Z38     0.000      0.0000       1.561         0.0       0.0000 log_k_JSE76     0.000      0.0000       0.000         0.8       0.0000 f_cyan_ilr_1    0.000      0.0000       0.000         0.0       0.6349 f_cyan_ilr_2    0.000      0.0000       0.000         0.0       0.0000 f_JCZ38_qlogis  0.000      0.0000       0.000         0.0       0.0000 f_JSE76_qlogis  0.000      0.0000       0.000         0.0       0.0000 log_k1          0.000      0.0000       0.000         0.0       0.0000 log_k2          0.000      0.0000       0.000         0.0       0.0000 g_qlogis        0.000      0.0000       0.000         0.0       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                0.000           0.00           0.00  0.000 0.0000 log_k_JCZ38           0.000           0.00           0.00  0.000 0.0000 log_k_J9Z38           0.000           0.00           0.00  0.000 0.0000 log_k_JSE76           0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_1          0.000           0.00           0.00  0.000 0.0000 f_cyan_ilr_2          1.797           0.00           0.00  0.000 0.0000 f_JCZ38_qlogis        0.000          13.86           0.00  0.000 0.0000 f_JSE76_qlogis        0.000           0.00          13.91  0.000 0.0000 log_k1                0.000           0.00           0.00  1.106 0.0000 log_k2                0.000           0.00           0.00  0.000 0.6141 g_qlogis              0.000           0.00           0.00  0.000 0.0000                g_qlogis cyan_0            0.000 log_k_JCZ38       0.000 log_k_J9Z38       0.000 log_k_JSE76       0.000 f_cyan_ilr_1      0.000 f_cyan_ilr_2      0.000 f_JCZ38_qlogis    0.000 f_JSE76_qlogis    0.000 log_k1            0.000 log_k2            0.000 g_qlogis          1.595  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2282 2274  -1121  Optimised parameters:                      est.   lower  upper cyan_0           102.6036      NA     NA log_k_JCZ38       -2.9348      NA     NA log_k_J9Z38       -5.1617      NA     NA log_k_JSE76       -3.6396      NA     NA f_cyan_ilr_1       0.6991      NA     NA f_cyan_ilr_2       0.6341      NA     NA f_JCZ38_qlogis  4232.3011      NA     NA f_JSE76_qlogis     1.9658      NA     NA log_k1            -1.9503      NA     NA log_k2            -4.4745      NA     NA g_qlogis          -0.4967      NA     NA a.1                2.7461 2.59274 2.8994 SD.log_k_JCZ38     1.3178 0.47602 2.1596 SD.log_k_J9Z38     0.7022 0.15061 1.2538 SD.log_k_JSE76     0.6566 0.15613 1.1570 SD.f_cyan_ilr_1    0.3409 0.11666 0.5652 SD.f_cyan_ilr_2    0.4385 0.09482 0.7821 SD.log_k1          0.7381 0.25599 1.2202 SD.log_k2          0.5133 0.18152 0.8450 SD.g_qlogis        0.9866 0.35681 1.6164  Correlation is not available  Random effects:                   est.   lower  upper SD.log_k_JCZ38  1.3178 0.47602 2.1596 SD.log_k_J9Z38  0.7022 0.15061 1.2538 SD.log_k_JSE76  0.6566 0.15613 1.1570 SD.f_cyan_ilr_1 0.3409 0.11666 0.5652 SD.f_cyan_ilr_2 0.4385 0.09482 0.7821 SD.log_k1       0.7381 0.25599 1.2202 SD.log_k2       0.5133 0.18152 0.8450 SD.g_qlogis     0.9866 0.35681 1.6164  Variance model:      est. lower upper a.1 2.746 2.593 2.899  Backtransformed parameters:                       est. lower upper cyan_0           1.026e+02    NA    NA k_JCZ38          5.314e-02    NA    NA k_J9Z38          5.732e-03    NA    NA k_JSE76          2.626e-02    NA    NA f_cyan_to_JCZ38  6.051e-01    NA    NA f_cyan_to_J9Z38  2.251e-01    NA    NA f_JCZ38_to_JSE76 1.000e+00    NA    NA f_JSE76_to_JCZ38 8.772e-01    NA    NA k1               1.422e-01    NA    NA k2               1.140e-02    NA    NA g                3.783e-01    NA    NA  Resulting formation fractions:                 ff cyan_JCZ38  0.6051 cyan_J9Z38  0.2251 cyan_sink   0.1698 JCZ38_JSE76 1.0000 JCZ38_sink  0.0000 JSE76_JCZ38 0.8772 JSE76_sink  0.1228  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   22.05 160.35    48.27   4.873   60.83 JCZ38  13.04  43.33       NA      NA      NA J9Z38 120.93 401.73       NA      NA      NA JSE76  26.39  87.68       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:02 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * cyan d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +            f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -            g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *            exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 898.534 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:         cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1        101.7393        -1.4493        -5.0118        -2.1269         0.6720    f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2          7.3362        13.4423        13.2659        -2.0061        -4.5527        g_qlogis         -0.5806   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 cyan_0          5.604        0.00       0.000       0.000       0.0000 log_k_JCZ38     0.000        2.77       0.000       0.000       0.0000 log_k_J9Z38     0.000        0.00       1.662       0.000       0.0000 log_k_JSE76     0.000        0.00       0.000       5.021       0.0000 f_cyan_ilr_1    0.000        0.00       0.000       0.000       0.6519 f_cyan_ilr_2    0.000        0.00       0.000       0.000       0.0000 f_JCZ38_qlogis  0.000        0.00       0.000       0.000       0.0000 f_JSE76_qlogis  0.000        0.00       0.000       0.000       0.0000 log_k1          0.000        0.00       0.000       0.000       0.0000 log_k2          0.000        0.00       0.000       0.000       0.0000 g_qlogis        0.000        0.00       0.000       0.000       0.0000                f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 cyan_0                 0.00           0.00           0.00 0.0000 0.0000 log_k_JCZ38            0.00           0.00           0.00 0.0000 0.0000 log_k_J9Z38            0.00           0.00           0.00 0.0000 0.0000 log_k_JSE76            0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_1           0.00           0.00           0.00 0.0000 0.0000 f_cyan_ilr_2          13.37           0.00           0.00 0.0000 0.0000 f_JCZ38_qlogis         0.00          14.21           0.00 0.0000 0.0000 f_JSE76_qlogis         0.00           0.00          14.58 0.0000 0.0000 log_k1                 0.00           0.00           0.00 0.8453 0.0000 log_k2                 0.00           0.00           0.00 0.0000 0.5969 g_qlogis               0.00           0.00           0.00 0.0000 0.0000                g_qlogis cyan_0             0.00 log_k_JCZ38        0.00 log_k_J9Z38        0.00 log_k_JSE76        0.00 f_cyan_ilr_1       0.00 f_cyan_ilr_2       0.00 f_JCZ38_qlogis     0.00 f_JSE76_qlogis     0.00 log_k1             0.00 log_k2             0.00 g_qlogis           1.69  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2237 2229  -1099  Optimised parameters:                      est.   lower   upper cyan_0          101.00243      NA      NA log_k_JCZ38      -2.80828      NA      NA log_k_J9Z38      -5.04449      NA      NA log_k_JSE76      -3.66981      NA      NA f_cyan_ilr_1      0.72564      NA      NA f_cyan_ilr_2      1.37978      NA      NA f_JCZ38_qlogis    1.98726      NA      NA f_JSE76_qlogis  414.80884      NA      NA log_k1           -2.38601      NA      NA log_k2           -4.63632      NA      NA g_qlogis         -0.33920      NA      NA a.1               2.10837 1.91261 2.30413 b.1               0.06223 0.05085 0.07361 SD.log_k_JCZ38    1.30902 0.48128 2.13675 SD.log_k_J9Z38    0.83882 0.25790 1.41974 SD.log_k_JSE76    0.58104 0.14201 1.02008 SD.f_cyan_ilr_1   0.35421 0.12398 0.58443 SD.f_cyan_ilr_2   0.79373 0.12007 1.46739 SD.log_k2         0.27476 0.08557 0.46394 SD.g_qlogis       0.96170 0.35463 1.56878  Correlation is not available  Random effects:                   est.   lower  upper SD.log_k_JCZ38  1.3090 0.48128 2.1367 SD.log_k_J9Z38  0.8388 0.25790 1.4197 SD.log_k_JSE76  0.5810 0.14201 1.0201 SD.f_cyan_ilr_1 0.3542 0.12398 0.5844 SD.f_cyan_ilr_2 0.7937 0.12007 1.4674 SD.log_k2       0.2748 0.08557 0.4639 SD.g_qlogis     0.9617 0.35463 1.5688  Variance model:        est.   lower   upper a.1 2.10837 1.91261 2.30413 b.1 0.06223 0.05085 0.07361  Backtransformed parameters:                       est. lower upper cyan_0           1.010e+02    NA    NA k_JCZ38          6.031e-02    NA    NA k_J9Z38          6.445e-03    NA    NA k_JSE76          2.548e-02    NA    NA f_cyan_to_JCZ38  6.808e-01    NA    NA f_cyan_to_J9Z38  2.440e-01    NA    NA f_JCZ38_to_JSE76 8.795e-01    NA    NA f_JSE76_to_JCZ38 1.000e+00    NA    NA k1               9.200e-02    NA    NA k2               9.693e-03    NA    NA g                4.160e-01    NA    NA  Resulting formation fractions:                  ff cyan_JCZ38  0.68081 cyan_J9Z38  0.24398 cyan_sink   0.07521 JCZ38_JSE76 0.87945 JCZ38_sink  0.12055 JSE76_JCZ38 1.00000 JSE76_sink  0.00000  Estimated disappearance times:         DT50   DT90 DT50back DT50_k1 DT50_k2 cyan   25.00 182.05     54.8   7.535   71.51 JCZ38  11.49  38.18       NA      NA      NA J9Z38 107.55 357.28       NA      NA      NA JSE76  27.20  90.36       NA      NA      NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:05:52 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 888.333 s Using 300, 100 iterations and 10 chains  Variance model: Constant variance   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               102.4395               -2.7673               -2.8942  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6201               -2.3107               -5.3123            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.7120                0.6754                1.1448         f_JCZ38_qlogis        f_JSE76_qlogis                14.8408               15.4734   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 4.589          0.0000                  0.00 log_k_cyan_free             0.000          0.4849                  0.00 log_k_cyan_free_bound       0.000          0.0000                  1.62 log_k_cyan_bound_free       0.000          0.0000                  0.00 log_k_JCZ38                 0.000          0.0000                  0.00 log_k_J9Z38                 0.000          0.0000                  0.00 log_k_JSE76                 0.000          0.0000                  0.00 f_cyan_ilr_1                0.000          0.0000                  0.00 f_cyan_ilr_2                0.000          0.0000                  0.00 f_JCZ38_qlogis              0.000          0.0000                  0.00 f_JSE76_qlogis              0.000          0.0000                  0.00                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000         0.0 log_k_cyan_free                       0.000      0.0000       0.000         0.0 log_k_cyan_free_bound                 0.000      0.0000       0.000         0.0 log_k_cyan_bound_free                 1.197      0.0000       0.000         0.0 log_k_JCZ38                           0.000      0.7966       0.000         0.0 log_k_J9Z38                           0.000      0.0000       1.561         0.0 log_k_JSE76                           0.000      0.0000       0.000         0.8 f_cyan_ilr_1                          0.000      0.0000       0.000         0.0 f_cyan_ilr_2                          0.000      0.0000       0.000         0.0 f_JCZ38_qlogis                        0.000      0.0000       0.000         0.0 f_JSE76_qlogis                        0.000      0.0000       0.000         0.0                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000            0.0           0.00 log_k_cyan_free             0.0000        0.000            0.0           0.00 log_k_cyan_free_bound       0.0000        0.000            0.0           0.00 log_k_cyan_bound_free       0.0000        0.000            0.0           0.00 log_k_JCZ38                 0.0000        0.000            0.0           0.00 log_k_J9Z38                 0.0000        0.000            0.0           0.00 log_k_JSE76                 0.0000        0.000            0.0           0.00 f_cyan_ilr_1                0.6349        0.000            0.0           0.00 f_cyan_ilr_2                0.0000        1.797            0.0           0.00 f_JCZ38_qlogis              0.0000        0.000           15.6           0.00 f_JSE76_qlogis              0.0000        0.000            0.0          17.52  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2280 2272  -1120  Optimised parameters:                               est.   lower  upper cyan_free_0               102.6532      NA     NA log_k_cyan_free            -2.8547      NA     NA log_k_cyan_free_bound      -2.7004      NA     NA log_k_cyan_bound_free      -3.5078      NA     NA log_k_JCZ38                -2.9255      NA     NA log_k_J9Z38                -5.1089      NA     NA log_k_JSE76                -3.6263      NA     NA f_cyan_ilr_1                0.6873      NA     NA f_cyan_ilr_2                0.6498      NA     NA f_JCZ38_qlogis           3624.2149      NA     NA f_JSE76_qlogis              1.9991      NA     NA a.1                         2.7472 2.55559 2.9388 SD.log_k_cyan_free          0.3227 0.10296 0.5423 SD.log_k_cyan_free_bound    0.8757 0.29525 1.4562 SD.log_k_cyan_bound_free    0.6128 0.20220 1.0233 SD.log_k_JCZ38              1.3431 0.48474 2.2014 SD.log_k_J9Z38              0.6881 0.14714 1.2291 SD.log_k_JSE76              0.6461 0.15321 1.1390 SD.f_cyan_ilr_1             0.3361 0.11376 0.5585 SD.f_cyan_ilr_2             0.4286 0.08419 0.7730  Correlation is not available  Random effects:                            est.   lower  upper SD.log_k_cyan_free       0.3227 0.10296 0.5423 SD.log_k_cyan_free_bound 0.8757 0.29525 1.4562 SD.log_k_cyan_bound_free 0.6128 0.20220 1.0233 SD.log_k_JCZ38           1.3431 0.48474 2.2014 SD.log_k_J9Z38           0.6881 0.14714 1.2291 SD.log_k_JSE76           0.6461 0.15321 1.1390 SD.f_cyan_ilr_1          0.3361 0.11376 0.5585 SD.f_cyan_ilr_2          0.4286 0.08419 0.7730  Variance model:      est. lower upper a.1 2.747 2.556 2.939  Backtransformed parameters:                           est. lower upper cyan_free_0          1.027e+02    NA    NA k_cyan_free          5.758e-02    NA    NA k_cyan_free_bound    6.718e-02    NA    NA k_cyan_bound_free    2.996e-02    NA    NA k_JCZ38              5.364e-02    NA    NA k_J9Z38              6.042e-03    NA    NA k_JSE76              2.662e-02    NA    NA f_cyan_free_to_JCZ38 6.039e-01    NA    NA f_cyan_free_to_J9Z38 2.285e-01    NA    NA f_JCZ38_to_JSE76     1.000e+00    NA    NA f_JSE76_to_JCZ38     8.807e-01    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g   0.1426  0.0121  0.3484   Resulting formation fractions:                     ff cyan_free_JCZ38 0.6039 cyan_free_J9Z38 0.2285 cyan_free_sink  0.1676 cyan_free       1.0000 JCZ38_JSE76     1.0000 JCZ38_sink      0.0000 JSE76_JCZ38     0.8807 JSE76_sink      0.1193  Estimated disappearance times:         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   23.84 154.95    46.65         4.86        57.31 JCZ38  12.92  42.93       NA           NA           NA J9Z38 114.71 381.07       NA           NA           NA JSE76  26.04  86.51       NA           NA           NA saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:21 2025  Date of summary: Mon May 12 21:06:22 2025   Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *            cyan_free + k_cyan_bound_free * cyan_bound d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *            cyan_bound d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38            * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38            * J9Z38 d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76  Data: 433 observations of 4 variable(s) grouped in 5 datasets  Model predictions using solution type deSolve   Fitted in 916.619 s Using 300, 100 iterations and 10 chains  Variance model: Two-component variance function   Starting values for degradation parameters:           cyan_free_0       log_k_cyan_free log_k_cyan_free_bound               101.7511               -2.8370               -3.0162  log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38                -3.6600               -2.2988               -5.3129            log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2                -3.6991                0.6722                4.8596         f_JCZ38_qlogis        f_JSE76_qlogis                13.4678               14.2149   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       cyan_free_0 log_k_cyan_free log_k_cyan_free_bound cyan_free_0                 5.629           0.000                 0.000 log_k_cyan_free             0.000           0.446                 0.000 log_k_cyan_free_bound       0.000           0.000                 1.449 log_k_cyan_bound_free       0.000           0.000                 0.000 log_k_JCZ38                 0.000           0.000                 0.000 log_k_J9Z38                 0.000           0.000                 0.000 log_k_JSE76                 0.000           0.000                 0.000 f_cyan_ilr_1                0.000           0.000                 0.000 f_cyan_ilr_2                0.000           0.000                 0.000 f_JCZ38_qlogis              0.000           0.000                 0.000 f_JSE76_qlogis              0.000           0.000                 0.000                       log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 cyan_free_0                           0.000      0.0000       0.000      0.0000 log_k_cyan_free                       0.000      0.0000       0.000      0.0000 log_k_cyan_free_bound                 0.000      0.0000       0.000      0.0000 log_k_cyan_bound_free                 1.213      0.0000       0.000      0.0000 log_k_JCZ38                           0.000      0.7801       0.000      0.0000 log_k_J9Z38                           0.000      0.0000       1.575      0.0000 log_k_JSE76                           0.000      0.0000       0.000      0.8078 f_cyan_ilr_1                          0.000      0.0000       0.000      0.0000 f_cyan_ilr_2                          0.000      0.0000       0.000      0.0000 f_JCZ38_qlogis                        0.000      0.0000       0.000      0.0000 f_JSE76_qlogis                        0.000      0.0000       0.000      0.0000                       f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis cyan_free_0                 0.0000        0.000           0.00           0.00 log_k_cyan_free             0.0000        0.000           0.00           0.00 log_k_cyan_free_bound       0.0000        0.000           0.00           0.00 log_k_cyan_bound_free       0.0000        0.000           0.00           0.00 log_k_JCZ38                 0.0000        0.000           0.00           0.00 log_k_J9Z38                 0.0000        0.000           0.00           0.00 log_k_JSE76                 0.0000        0.000           0.00           0.00 f_cyan_ilr_1                0.6518        0.000           0.00           0.00 f_cyan_ilr_2                0.0000        9.981           0.00           0.00 f_JCZ38_qlogis              0.0000        0.000          14.26           0.00 f_JSE76_qlogis              0.0000        0.000           0.00          16.17  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling    AIC  BIC logLik   2241 2233  -1101  Optimised parameters:                               est.   lower   upper cyan_free_0              100.95469      NA      NA log_k_cyan_free           -3.18706      NA      NA log_k_cyan_free_bound     -3.38455      NA      NA log_k_cyan_bound_free     -3.75788      NA      NA log_k_JCZ38               -2.77024      NA      NA log_k_J9Z38               -5.03665      NA      NA log_k_JSE76               -3.60289      NA      NA f_cyan_ilr_1               0.72263      NA      NA f_cyan_ilr_2               1.45352      NA      NA f_JCZ38_qlogis             2.00778      NA      NA f_JSE76_qlogis           941.58570      NA      NA a.1                        2.11130 1.91479 2.30780 b.1                        0.06299 0.05152 0.07445 SD.log_k_cyan_free         0.50098 0.18805 0.81390 SD.log_k_cyan_bound_free   0.31671 0.08467 0.54875 SD.log_k_JCZ38             1.25865 0.45932 2.05798 SD.log_k_J9Z38             0.86833 0.27222 1.46444 SD.log_k_JSE76             0.59325 0.14711 1.03940 SD.f_cyan_ilr_1            0.35705 0.12521 0.58890 SD.f_cyan_ilr_2            0.88541 0.13797 1.63286  Correlation is not available  Random effects:                            est.   lower  upper SD.log_k_cyan_free       0.5010 0.18805 0.8139 SD.log_k_cyan_bound_free 0.3167 0.08467 0.5487 SD.log_k_JCZ38           1.2587 0.45932 2.0580 SD.log_k_J9Z38           0.8683 0.27222 1.4644 SD.log_k_JSE76           0.5933 0.14711 1.0394 SD.f_cyan_ilr_1          0.3571 0.12521 0.5889 SD.f_cyan_ilr_2          0.8854 0.13797 1.6329  Variance model:        est.   lower   upper a.1 2.11130 1.91479 2.30780 b.1 0.06299 0.05152 0.07445  Backtransformed parameters:                           est. lower upper cyan_free_0          1.010e+02    NA    NA k_cyan_free          4.129e-02    NA    NA k_cyan_free_bound    3.389e-02    NA    NA k_cyan_bound_free    2.333e-02    NA    NA k_JCZ38              6.265e-02    NA    NA k_J9Z38              6.495e-03    NA    NA k_JSE76              2.724e-02    NA    NA f_cyan_free_to_JCZ38 6.844e-01    NA    NA f_cyan_free_to_J9Z38 2.463e-01    NA    NA f_JCZ38_to_JSE76     8.816e-01    NA    NA f_JSE76_to_JCZ38     1.000e+00    NA    NA  Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2  cyan_g  0.08751 0.01101 0.39586   Resulting formation fractions:                      ff cyan_free_JCZ38 0.68444 cyan_free_J9Z38 0.24633 cyan_free_sink  0.06923 cyan_free       1.00000 JCZ38_JSE76     0.88161 JCZ38_sink      0.11839 JSE76_JCZ38     1.00000 JSE76_sink      0.00000  Estimated disappearance times:         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 cyan   25.36 163.36    49.18        7.921        62.95 JCZ38  11.06  36.75       NA           NA           NA J9Z38 106.71 354.49       NA           NA           NA JSE76  25.44  84.51       NA           NA           NA"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] sass_0.4.9        generics_0.1.3    lattice_0.22-6    digest_0.6.37      [5] magrittr_2.0.3    evaluate_1.0.3    grid_4.5.0        fastmap_1.2.0      [9] cellranger_1.1.0  jsonlite_1.9.0    processx_3.8.6    pkgbuild_1.4.6    [13] deSolve_1.40      mclust_6.1.1      ps_1.9.0          gridExtra_2.3     [17] scales_1.3.0      codetools_0.2-20  textshaping_1.0.0 jquerylib_0.1.4   [21] cli_3.6.4         rlang_1.1.5       munsell_0.5.1     cachem_1.1.0      [25] yaml_2.3.10       inline_0.3.21     tools_4.5.0       dplyr_1.1.4       [29] colorspace_2.1-1  ggplot2_3.5.1     vctrs_0.6.5       R6_2.6.1          [33] zoo_1.8-13        lifecycle_1.0.4   fs_1.6.5          htmlwidgets_1.6.4 [37] MASS_7.3-65       ragg_1.3.3        pkgconfig_2.0.3   desc_1.4.3        [41] callr_3.7.6       pkgdown_2.1.1     pillar_1.10.1     bslib_0.9.0       [45] gtable_0.3.6      glue_1.8.0        systemfonts_1.2.1 xfun_0.51         [49] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.1  htmltools_0.5.8.1 [53] nlme_3.1-168      compiler_4.5.0    readxl_1.4.4"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing hierarchical pathway kinetics with residue data on cyantraniliprole","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"purpose document demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package. assembled course work package 1.1 Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) German Environment Agency carried 2022 2023. mkin package used version 1.2.10. contains test data functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"data","dir":"Articles > Prebuilt","previous_headings":"","what":"Data","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"test data available mkin package object class mkindsg (mkin dataset group) identifier dimethenamid_2018. following preprocessing steps still necessary: data available enantiomer dimethenamid-P (DMTAP) renamed substance name data racemic mixture dimethenamid (DMTA). reason difference degradation behaviour identified EU risk assessment. data transformation products unnecessary columns discarded observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters independent temperature Finally, datasets observed soil (Elliot 1 Elliot 2) combined, resulting dimethenamid (DMTA) data six soils. following commented R code performs preprocessing. following tables show 6 datasets. Dataset Calke Dataset Borstel Dataset Flaach Dataset BBA 2.2 Dataset BBA 2.3 Dataset Elliot","code":"# Apply a function to each of the seven datasets in the mkindsg object to create a list dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data                     # Get a dataset   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"              # Rename DMTAP to DMTA   ds_i <- subset(ds_i, name == \"DMTA\", c(\"name\", \"time\", \"value\")) # Select data   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]  # Normalise time   ds_i                                                       # Return the dataset })  # Use dataset titles as names for the list elements names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)  # Combine data for Elliot soil to obtain a named list with six elements dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) # dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL for (ds_name in names(dmta_ds)) {     print(kable(mkin_long_to_wide(dmta_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       label = paste0(\"tab:\", ds_name), booktabs = TRUE))     cat(\"\\n\\\\clearpage\\n\") }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"separate-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Separate evaluations","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"order obtain suitable starting parameters NLHM fits, separate fits four models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. table , OK indicates convergence, C indicates failure converge. separate fits constant variance converged, sole exception HS fit BBA 2.2 data. prepare fitting NLHM using two-component error model, separate fits updated assuming two-component error. Using two-component error model, one fit converge constant variance converge, non-SFO fits failed converge.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\") f_sep_const <- mmkin(   deg_mods,   dmta_ds,   error_model = \"const\",   quiet = TRUE)  status(f_sep_const) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hierarchichal-model-fits","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchichal model fits","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"following code fits eight versions hierarchical models data, using SFO, FOMC, DFOP HS parent compound, using either constant variance two-component error error model. default parameter distribution model mkin allows variation degradation parameters across assumed population soils. words, degradation parameter associated random effect first step. mhmkin function makes possible fit eight versions parallel (given sufficient number computing cores available) save execution time. Convergence plots summaries fits shown appendix. output status function shows fits terminated successfully. AIC BIC values show biphasic models DFOP HS give best fits. DFOP model preferred , better mechanistic basis batch experiments constant incubation conditions. Also, shows lowest AIC BIC values first set fits combined two-component error model. Therefore, DFOP model selected refinements fits aim make model fully identifiable.","code":"f_saem <- mhmkin(list(f_sep_const, f_sep_tc), transformations = \"saemix\") status(f_saem) |> kable() anova(f_saem) |> kable(digits = 1)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"parameter-identifiability-based-on-the-fisher-information-matrix","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Parameter identifiability based on the Fisher Information Matrix","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Using illparms function, ill-defined statistical model parameters standard deviations degradation parameters population error model parameters can found. According illparms function, fitted standard deviation second kinetic rate constant k2 ill-defined DFOP fits. suggests different values obtained standard deviation using different starting values. thus identified overparameterisation addressed removing random effect k2 parameter model. resulting fit, checked whether still ill-defined parameters, case. , refined model compared previous best model. model without random effect k2 reduced version previous model. Therefore, models nested can compared using likelihood ratio test. achieved argument test = TRUE anova function. AIC BIC criteria lower removal ill-defined random effect k2. p value likelihood ratio test much greater 0.05, indicating model higher likelihood (model random effects degradation parameters f_saem[[\"DFOP\", \"tc\"]]) fit significantly better model lower likelihood (reduced model f_saem_dfop_tc_no_ranef_k2). Therefore, AIC, BIC likelihood ratio test suggest use reduced model. convergence fit checked visually. Convergence plot NLHM DFOP fit two-component error without random effect ‘k2’ parameters appear converged satisfactory degree. final fit plotted using plot method mkin package. Plot final NLHM DFOP fit Finally, summary report fit produced.","code":"illparms(f_saem) |> kable() f_saem_dfop_tc_no_ranef_k2 <- update(f_saem[[\"DFOP\", \"tc\"]],   no_random_effect = \"k2\") illparms(f_saem_dfop_tc_no_ranef_k2) anova(f_saem[[\"DFOP\", \"tc\"]], f_saem_dfop_tc_no_ranef_k2, test = TRUE) |>   kable(format.args = list(digits = 4)) plot(f_saem_dfop_tc_no_ranef_k2) summary(f_saem_dfop_tc_no_ranef_k2) saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:46 2025  Date of summary: Mon May 12 21:06:46 2025   Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 3.953 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0       k1       k2        g  98.71186  0.08675  0.01374  0.93491   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 g DMTA_0  98.71  0  0 0 k1       0.00  1  0 0 k2       0.00  0  1 0 g        0.00  0  0 1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   663.7 661.8 -322.9  Optimised parameters:                est.     lower     upper DMTA_0    98.256267 96.286112 100.22642 k1         0.064037  0.033281   0.09479 k2         0.008469  0.006002   0.01094 g          0.954167  0.914460   0.99387 a.1        1.061795  0.878608   1.24498 b.1        0.029550  0.022593   0.03651 SD.DMTA_0  2.068581  0.427178   3.70998 SD.k1      0.598285  0.258235   0.93833 SD.g       1.016689  0.360061   1.67332  Correlation:     DMTA_0  k1      k2      k1  0.0213                 k2  0.0541  0.0344         g  -0.0521 -0.0286 -0.2744  Random effects:             est.  lower  upper SD.DMTA_0 2.0686 0.4272 3.7100 SD.k1     0.5983 0.2582 0.9383 SD.g      1.0167 0.3601 1.6733  Variance model:        est.   lower   upper a.1 1.06180 0.87861 1.24498 b.1 0.02955 0.02259 0.03651  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 11.45 41.32    12.44   10.82   81.85"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"alternative-check-of-parameter-identifiability","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Alternative check of parameter identifiability","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"parameter check used illparms function based quadratic approximation likelihood surface near optimum, calculated using Fisher Information Matrix (FIM). alternative way check parameter identifiability (Duchesne et al. 2021) based multistart approach recently implemented mkin. graph shows boxplots parameters obtained 50 runs saem algorithm different parameter combinations, sampled range parameters obtained individual datasets fitted separately using nonlinear regression. Scaled parameters multistart runs, full model graph clearly confirms lack identifiability variance k2 full model. overparameterisation model also indicates lack identifiability variance parameter g. parameter boxplots multistart runs reduced model shown indicate runs give similar results, regardless starting parameters. Scaled parameters multistart runs, reduced model parameters top 25% fits shown (based feature introduced mkin 1.2.2 currently development), scatter even less shown . Scaled parameters multistart runs, reduced model, fits top 25% likelihood values","code":"f_saem_dfop_tc_multi <- multistart(f_saem[[\"DFOP\", \"tc\"]], n = 50, cores = 15) par(mar = c(6.1, 4.1, 2.1, 2.1)) parplot(f_saem_dfop_tc_multi, lpos = \"bottomright\", ylim = c(0.3, 10), las = 2) f_saem_dfop_tc_no_ranef_k2_multi <- multistart(f_saem_dfop_tc_no_ranef_k2,   n = 50, cores = 15) par(mar = c(6.1, 4.1, 2.1, 2.1)) parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2,   lpos = \"bottomright\") par(mar = c(6.1, 4.1, 2.1, 2.1)) parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2, llquant = 0.25,   lpos = \"bottomright\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"conclusions","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusions","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Fitting four parent degradation models SFO, FOMC, DFOP HS part hierarchical model fits two different error models normal distributions transformed degradation parameters works without technical problems. biphasic models DFOP HS gave best fit data, default parameter distribution model fully identifiable. Removing random effect second kinetic rate constant DFOP model resulted reduced model fully identifiable showed lowest values model selection criteria AIC BIC. reliability identification model parameters confirmed using multiple starting values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"acknowledgements","dir":"Articles > Prebuilt","previous_headings":"","what":"Acknowledgements","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"helpful comments Janina Wöltjen German Environment Agency gratefully acknowledged.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hierarchical-model-fit-listings","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hierarchical model fit listings","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Hierarchical mkin fit SFO model error model const  Hierarchical mkin fit SFO model error model tc  Hierarchical mkin fit FOMC model error model const  Hierarchical mkin fit FOMC model error model tc  Hierarchical mkin fit DFOP model error model const  Hierarchical mkin fit DFOP model error model tc  Hierarchical mkin fit HS model error model const  Hierarchical mkin fit HS model error model tc","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:38 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - k_DMTA * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 0.793 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:  DMTA_0  k_DMTA  97.2953  0.0566   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k_DMTA DMTA_0   97.3      0 k_DMTA    0.0      1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   796.3 795.3 -393.2  Optimised parameters:               est.    lower   upper DMTA_0    97.28130 95.71113 98.8515 k_DMTA     0.05665  0.02909  0.0842 a.1        2.66442  2.35579  2.9731 SD.DMTA_0  1.54776  0.15447  2.9411 SD.k_DMTA  0.60690  0.26248  0.9513  Correlation:         DMTA_0 k_DMTA 0.0168  Random effects:             est.  lower  upper SD.DMTA_0 1.5478 0.1545 2.9411 SD.k_DMTA 0.6069 0.2625 0.9513  Variance model:      est. lower upper a.1 2.664 2.356 2.973  Estimated disappearance times:       DT50  DT90 DMTA 12.24 40.65 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:40 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - k_DMTA * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 2.462 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0   k_DMTA  96.99175  0.05603   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k_DMTA DMTA_0  96.99      0 k_DMTA   0.00      1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   798.3 797.1 -393.2  Optimised parameters:                est.    lower    upper DMTA_0    97.271822 95.70316 98.84049 k_DMTA     0.056638  0.02911  0.08417 a.1        2.660081  2.27492  3.04525 b.1        0.001665 -0.14451  0.14784 SD.DMTA_0  1.545520  0.14301  2.94803 SD.k_DMTA  0.606422  0.26227  0.95057  Correlation:         DMTA_0 k_DMTA 0.0169  Random effects:             est.  lower  upper SD.DMTA_0 1.5455 0.1430 2.9480 SD.k_DMTA 0.6064 0.2623 0.9506  Variance model:         est.   lower  upper a.1 2.660081  2.2749 3.0452 b.1 0.001665 -0.1445 0.1478  Estimated disappearance times:       DT50  DT90 DMTA 12.24 40.65 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:39 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 1.408 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:  DMTA_0   alpha    beta   98.292   9.909 156.341   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 alpha beta DMTA_0  98.29     0    0 alpha    0.00     1    0 beta     0.00     0    1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   734.2 732.7 -360.1  Optimised parameters:               est.   lower   upper DMTA_0     98.3435 96.9033  99.784 alpha       7.2007  2.5889  11.812 beta      112.8745 34.8816 190.867 a.1         2.0459  1.8054   2.286 SD.DMTA_0   1.4795  0.2717   2.687 SD.alpha    0.6396  0.1509   1.128 SD.beta     0.6874  0.1587   1.216  Correlation:        DMTA_0  alpha   alpha -0.1125         beta  -0.1227  0.3632  Random effects:             est.  lower upper SD.DMTA_0 1.4795 0.2717 2.687 SD.alpha  0.6396 0.1509 1.128 SD.beta   0.6874 0.1587 1.216  Variance model:      est. lower upper a.1 2.046 1.805 2.286  Estimated disappearance times:       DT50  DT90 DT50back DMTA 11.41 42.53     12.8 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:40 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 2.735 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters: DMTA_0  alpha   beta  98.772  4.663 92.597   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 alpha beta DMTA_0  98.77     0    0 alpha    0.00     1    0 beta     0.00     0    1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   720.7 719.1 -352.4  Optimised parameters:               est.    lower     upper DMTA_0    99.10577 97.33296 100.87859 alpha      5.46260  2.52199   8.40321 beta      81.66080 30.46664 132.85497 a.1        1.50219  1.25801   1.74636 b.1        0.02893  0.02048   0.03739 SD.DMTA_0  1.61887 -0.03843   3.27618 SD.alpha   0.58145  0.17364   0.98925 SD.beta    0.68205  0.21108   1.15302  Correlation:        DMTA_0  alpha   alpha -0.1321         beta  -0.1430  0.2467  Random effects:             est.    lower  upper SD.DMTA_0 1.6189 -0.03843 3.2762 SD.alpha  0.5814  0.17364 0.9892 SD.beta   0.6821  0.21108 1.1530  Variance model:        est.   lower   upper a.1 1.50219 1.25801 1.74636 b.1 0.02893 0.02048 0.03739  Estimated disappearance times:       DT50  DT90 DT50back DMTA 11.05 42.81    12.89 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:39 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 1.769 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:   DMTA_0       k1       k2        g  98.64383  0.09211  0.02999  0.76814   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 g DMTA_0  98.64  0  0 0 k1       0.00  1  0 0 k2       0.00  0  1 0 g        0.00  0  0 1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC BIC logLik   711.8 710 -346.9  Optimised parameters:                est.     lower    upper DMTA_0    98.092481 96.573899 99.61106 k1         0.062499  0.030336  0.09466 k2         0.009065 -0.005133  0.02326 g          0.948967  0.862080  1.03586 a.1        1.821671  1.604774  2.03857 SD.DMTA_0  1.677785  0.472066  2.88350 SD.k1      0.634962  0.270788  0.99914 SD.k2      1.033498 -0.205994  2.27299 SD.g       1.710046  0.428642  2.99145  Correlation:     DMTA_0  k1      k2      k1  0.0246                 k2  0.0491  0.0953         g  -0.0552 -0.0889 -0.4795  Random effects:            est.   lower  upper SD.DMTA_0 1.678  0.4721 2.8835 SD.k1     0.635  0.2708 0.9991 SD.k2     1.033 -0.2060 2.2730 SD.g      1.710  0.4286 2.9914  Variance model:      est. lower upper a.1 1.822 1.605 2.039  Estimated disappearance times:       DT50 DT90 DT50back DT50_k1 DT50_k2 DMTA 11.79 42.8    12.88   11.09   76.46 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:41 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 3.069 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0       k1       k2        g  98.71186  0.08675  0.01374  0.93491   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 g DMTA_0  98.71  0  0 0 k1       0.00  1  0 0 k2       0.00  0  1 0 g        0.00  0  0 1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   665.7 663.6 -322.9  Optimised parameters:                est.     lower     upper DMTA_0    98.347470 96.380815 100.31413 k1         0.064524  0.034279   0.09477 k2         0.008304  0.005843   0.01076 g          0.952128  0.909578   0.99468 a.1        1.068907  0.883665   1.25415 b.1        0.029265  0.022318   0.03621 SD.DMTA_0  2.065796  0.427951   3.70364 SD.k1      0.583703  0.251796   0.91561 SD.k2      0.004167 -7.831228   7.83956 SD.g       1.064450  0.397479   1.73142  Correlation:     DMTA_0  k1      k2      k1  0.0223                 k2  0.0568  0.0394         g  -0.0464 -0.0269 -0.2713  Random effects:               est.   lower  upper SD.DMTA_0 2.065796  0.4280 3.7036 SD.k1     0.583703  0.2518 0.9156 SD.k2     0.004167 -7.8312 7.8396 SD.g      1.064450  0.3975 1.7314  Variance model:        est.   lower   upper a.1 1.06891 0.88367 1.25415 b.1 0.02927 0.02232 0.03621  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 11.39 41.36    12.45   10.74   83.48 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:39 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 1.996 s Using 300, 100 iterations and 9 chains  Variance model: Constant variance   Starting values for degradation parameters:   DMTA_0       k1       k2       tb  97.82176  0.06931  0.02997 11.13945   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 tb DMTA_0  97.82  0  0  0 k1       0.00  1  0  0 k2       0.00  0  1  0 tb       0.00  0  0  1  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling   AIC   BIC logLik   714 712.1   -348  Optimised parameters:               est.    lower    upper DMTA_0    98.16102 96.47747 99.84456 k1         0.07876  0.05261  0.10491 k2         0.02227  0.01706  0.02747 tb        13.99089 -7.40049 35.38228 a.1        1.82305  1.60700  2.03910 SD.DMTA_0  1.88413  0.56204  3.20622 SD.k1      0.34292  0.10482  0.58102 SD.k2      0.19851  0.01718  0.37985 SD.tb      1.68168  0.58064  2.78272  Correlation:     DMTA_0  k1      k2      k1  0.0142                 k2  0.0001 -0.0025         tb  0.0165 -0.1256 -0.0301  Random effects:             est.   lower  upper SD.DMTA_0 1.8841 0.56204 3.2062 SD.k1     0.3429 0.10482 0.5810 SD.k2     0.1985 0.01718 0.3798 SD.tb     1.6817 0.58064 2.7827  Variance model:      est. lower upper a.1 1.823 1.607 2.039  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 8.801 67.91    20.44   8.801   31.13 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Mon May 12 21:06:41 2025  Date of summary: Mon May 12 21:07:51 2025   Equations: d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Model predictions using solution type analytical   Fitted in 3.389 s Using 300, 100 iterations and 9 chains  Variance model: Two-component variance function   Starting values for degradation parameters:   DMTA_0       k1       k2       tb  98.45190  0.07525  0.02576 19.19375   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        DMTA_0 k1 k2 tb DMTA_0  98.45  0  0  0 k1       0.00  1  0  0 k2       0.00  0  1  0 tb       0.00  0  0  1  Starting values for error model parameters: a.1 b.1    1   1   Results:  Likelihood computed by importance sampling     AIC BIC logLik   667.1 665 -323.6  Optimised parameters:               est.    lower    upper DMTA_0    97.76571 95.81350 99.71791 k1         0.05855  0.03080  0.08630 k2         0.02337  0.01664  0.03010 tb        31.09638 29.38289 32.80987 a.1        1.08835  0.90059  1.27611 b.1        0.02964  0.02261  0.03667 SD.DMTA_0  2.04877  0.42553  3.67200 SD.k1      0.59166  0.25621  0.92711 SD.k2      0.30698  0.09561  0.51835 SD.tb      0.01274 -0.10915  0.13464  Correlation:     DMTA_0  k1      k2      k1  0.0160                 k2 -0.0070 -0.0024         tb -0.0668 -0.0103 -0.2013  Random effects:              est.    lower  upper SD.DMTA_0 2.04877  0.42553 3.6720 SD.k1     0.59166  0.25621 0.9271 SD.k2     0.30698  0.09561 0.5183 SD.tb     0.01274 -0.10915 0.1346  Variance model:        est.   lower   upper a.1 1.08835 0.90059 1.27611 b.1 0.02964 0.02261 0.03667  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 DMTA 11.84 51.71    15.57   11.84   29.66"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hierarchical-model-convergence-plots","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hierarchical model convergence plots","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"Convergence plot NLHM SFO fit constant variance Convergence plot NLHM SFO fit two-component error Convergence plot NLHM FOMC fit constant variance Convergence plot NLHM FOMC fit two-component error Convergence plot NLHM DFOP fit constant variance Convergence plot NLHM DFOP fit two-component error Convergence plot NLHM HS fit constant variance Convergence plot NLHM HS fit two-component error","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  gridExtra_2.3     jquerylib_0.1.4   systemfonts_1.2.1  [9] scales_1.3.0      textshaping_1.0.0 yaml_2.3.10       fastmap_1.2.0     [13] lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          generics_0.1.3    [17] lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 tibble_3.2.1      [21] desc_1.4.3        munsell_0.5.1     bslib_0.9.0       pillar_1.10.1     [25] rlang_1.1.5       cachem_1.1.0      xfun_0.51         fs_1.6.5          [29] sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     magrittr_2.0.3    [33] digest_0.6.37     grid_4.5.0        mclust_6.1.1      lifecycle_1.0.4   [37] nlme_3.1-168      vctrs_0.6.5       evaluate_1.0.3    glue_1.8.0        [41] codetools_0.2-20  ragg_1.3.3        zoo_1.8-13        colorspace_2.1-1  [45] tools_4.5.0       pkgconfig_2.0.3   htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS, parallel formation two metabolites can fitted mkin package. assembled course work package 1.2 Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) German Environment Agency carried 2022 2023. mkin package used version 1.2.10, currently development. contains test data, functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores()  # We need to start a new cluster after defining a compiled model that is # saved as a DLL to the user directory, therefore we define a function # This is used again after defining the pathway model start_cluster <- function(n_cores) {   if (Sys.info()[\"sysname\"] == \"Windows\") {     ret <- makePSOCKcluster(n_cores)   } else {     ret <- makeForkCluster(n_cores)   }   return(ret) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"data","dir":"Articles > Prebuilt","previous_headings":"","what":"Data","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"test data available mkin package object class mkindsg (mkin dataset group) identifier dimethenamid_2018. following preprocessing steps done document. data available enantiomer dimethenamid-P (DMTAP) renamed substance name data racemic mixture dimethenamid (DMTA). reason difference degradation behaviour identified EU risk assessment. Unnecessary columns discarded observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters independent temperature Finally, datasets observed soil (Elliot 1 Elliot 2) combined, resulting dimethenamid (DMTA) data six soils. following commented R code performs preprocessing. following tables show 6 datasets. Dataset Calke Dataset Borstel Dataset Flaach Dataset BBA 2.2 Dataset BBA 2.3 Dataset Elliot","code":"# Apply a function to each of the seven datasets in the mkindsg object to create a list dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data                     # Get a dataset   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"              # Rename DMTAP to DMTA   ds_i <- subset(ds_i, select = c(\"name\", \"time\", \"value\")) # Select data   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]  # Normalise time   ds_i                                                       # Return the dataset })  # Use dataset titles as names for the list elements names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)  # Combine data for Elliot soil to obtain a named list with six elements dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) # dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL for (ds_name in names(dmta_ds)) {   print(     kable(mkin_long_to_wide(dmta_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE))     cat(\"\\n\\\\clearpage\\n\") }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"separate-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Separate evaluations","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"first step obtain suitable starting parameters NLHM fits, separate fits several variants pathway model used previously (Ranke et al. 2021), varying kinetic model parent compound. SFORB model often provides faster convergence DFOP model, can sometimes fitted DFOP model results errors, included set parent models tested . separate pathway fits SFO FOMC parent constant variance converged (status OK). fits DFOP SFORB parent converged well. fits HS parent converge default settings. two-component error model, set fits convergence problems slightly different, convergence problems appearing different data sets applying DFOP SFORB model additional convergence problems using FOMC model parent.","code":"if (!dir.exists(\"dmta_dlls\")) dir.create(\"dmta_dlls\") m_sfo_path_1 <- mkinmod(   DMTA = mkinsub(\"SFO\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_sfo_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_fomc_path_1 <- mkinmod(   DMTA = mkinsub(\"FOMC\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_fomc_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_dfop_path_1 <- mkinmod(   DMTA = mkinsub(\"DFOP\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_dfop_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_sforb_path_1 <- mkinmod(   DMTA = mkinsub(\"SFORB\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_sforb_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) m_hs_path_1 <- mkinmod(   DMTA = mkinsub(\"HS\", c(\"M23\", \"M27\", \"M31\")),   M23 = mkinsub(\"SFO\"),   M27 = mkinsub(\"SFO\"),   M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),   name = \"m_hs_path\", dll_dir = \"dmta_dlls\",   unload = TRUE, overwrite = TRUE,   quiet = TRUE ) cl <- start_cluster(n_cores)  deg_mods_1 <- list(   sfo_path_1 = m_sfo_path_1,   fomc_path_1 = m_fomc_path_1,   dfop_path_1 = m_dfop_path_1,   sforb_path_1 = m_sforb_path_1,   hs_path_1 = m_hs_path_1)  sep_1_const <- mmkin(   deg_mods_1,   dmta_ds,   error_model = \"const\",   quiet = TRUE)  status(sep_1_const) |> kable() sep_1_tc <- update(sep_1_const, error_model = \"tc\") status(sep_1_tc) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"hierarchichal-model-fits","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchichal model fits","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"following code fits two sets corresponding hierarchical models data, one assuming constant variance, one assuming two-component error. run time fits around two hours five year old hardware. recent hardware upgrade fits complete less twenty minutes. According status function, fits terminated successfully. goodness--fit models compared, warning obtained, indicating likelihood pathway fit SFORB parent compound constant variance calculated importance sampling (method ‘’). default method AIC BIC comparisons based, variant included model comparison table. Comparing goodness--fit remaining models, HS model model two-component error provides best fit. However, batch experiments performed constant conditions experiments evaluated , reason assume discontinuity, SFORB model preferable mechanistic viewpoint. addition, information criteria AIC BIC similar HS SFORB. Therefore, SFORB model selected refinements.","code":"saem_1 <- mhmkin(list(sep_1_const, sep_1_tc)) status(saem_1) |> kable() anova(saem_1) |> kable(digits = 1)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"parameter-identifiability-based-on-the-fisher-information-matrix","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Parameter identifiability based on the Fisher Information Matrix","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"Using illparms function, ill-defined statistical model parameters standard deviations degradation parameters population error model parameters can found. using constant variance, ill-defined variance parameters identified illparms function degradation models. using two-component error model, one ill-defined variance parameter variants except variant using DFOP parent compound. selected combination SFORB pathway model two-component error, random effect rate constant reversibly bound DMTA free DMTA (k_DMTA_bound_free) well-defined. Therefore, fit updated without assuming random effect parameter. expected, ill-defined parameters remain. model comparison shows reduced model preferable. convergence plot refined fit shown .  parameters, example f_DMTA_ilr_1 f_DMTA_ilr_2, .e. two parameters determining formation fractions parallel formation three metabolites, movement parameters still visible second phase algorithm. However, amplitude movement range amplitude towards end first phase. Therefore, likely increase iterations improve parameter estimates much, proposed fit acceptable. numeric convergence criterion implemented saemix.","code":"illparms(saem_1) |> kable() saem_sforb_path_1_tc_reduced <- update(saem_1[[\"sforb_path_1\", \"tc\"]],   no_random_effect = \"log_k_DMTA_bound_free\") illparms(saem_sforb_path_1_tc_reduced) anova(saem_1[[\"sforb_path_1\", \"tc\"]], saem_sforb_path_1_tc_reduced) |> kable(digits = 1) plot(saem_sforb_path_1_tc_reduced$so, plot.type = \"convergence\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"alternative-check-of-parameter-identifiability","dir":"Articles > Prebuilt","previous_headings":"Hierarchichal model fits","what":"Alternative check of parameter identifiability","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"alternative check parameter identifiability (Duchesne et al. 2021), multistart runs performed basis refined fit shown . 32 fits initiated, 17 terminated without error. reason wide variation starting parameters combination parameter variation used SAEM algorithm leads parameter combinations degradation model numerical integration routine cope . variation initial parameters, model fits take two times time original fit. Parameter boxplots multistart runs succeeded However, visual analysis boxplot parameters obtained successful fits confirms results sufficiently independent starting parameters, remaining ill-defined parameters.","code":"saem_sforb_path_1_tc_reduced_multi <- multistart(saem_sforb_path_1_tc_reduced,   n = 32, cores = 10) (subscript) logical subscript too long print(saem_sforb_path_1_tc_reduced_multi) <multistart> object with 32 fits:  E OK   7 25  OK: Fit terminated successfully E: Error par(mar = c(12.1, 4.1, 2.1, 2.1)) parplot(saem_sforb_path_1_tc_reduced_multi, ylim = c(0.5, 2), las = 2)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"plots-of-selected-fits","dir":"Articles > Prebuilt","previous_headings":"","what":"Plots of selected fits","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"SFORB pathway fits full reduced parameter distribution model shown . SFORB pathway fit two-component error SFORB pathway fit two-component error, reduced parameter model Plots remaining fits listings successful fits shown Appendix.","code":"plot(saem_1[[\"sforb_path_1\", \"tc\"]]) plot(saem_sforb_path_1_tc_reduced) stopCluster(cl)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"conclusions","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusions","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"Pathway fits SFO, FOMC, DFOP, SFORB HS models parent compound successfully performed.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"acknowledgements","dir":"Articles > Prebuilt","previous_headings":"","what":"Acknowledgements","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"helpful comments Janina Wöltjen German Environment Agency earlier versions document gratefully acknowledged.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"plots-of-hierarchical-fits-not-selected-for-refinement","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Plots of hierarchical fits not selected for refinement","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"SFO pathway fit two-component error FOMC pathway fit two-component error HS pathway fit two-component error","code":"plot(saem_1[[\"sfo_path_1\", \"tc\"]]) plot(saem_1[[\"fomc_path_1\", \"tc\"]]) plot(saem_1[[\"sforb_path_1\", \"tc\"]])"},{"path":[]},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] sass_0.4.9        generics_0.1.3    lattice_0.22-6    digest_0.6.37      [5] magrittr_2.0.3    evaluate_1.0.3    grid_4.5.0        fastmap_1.2.0      [9] jsonlite_1.9.0    processx_3.8.6    pkgbuild_1.4.6    deSolve_1.40      [13] mclust_6.1.1      ps_1.9.0          gridExtra_2.3     scales_1.3.0      [17] codetools_0.2-20  textshaping_1.0.0 jquerylib_0.1.4   cli_3.6.4         [21] rlang_1.1.5       munsell_0.5.1     cachem_1.1.0      yaml_2.3.10       [25] tools_4.5.0       inline_0.3.21     dplyr_1.1.4       colorspace_2.1-1  [29] ggplot2_3.5.1     vctrs_0.6.5       R6_2.6.1          zoo_1.8-13        [33] lifecycle_1.0.4   fs_1.6.5          htmlwidgets_1.6.4 MASS_7.3-65       [37] ragg_1.3.3        pkgconfig_2.0.3   desc_1.4.3        callr_3.7.6       [41] pkgdown_2.1.1     pillar_1.10.1     bslib_0.9.0       gtable_0.3.6      [45] glue_1.8.0        systemfonts_1.2.1 xfun_0.51         tibble_3.2.1      [49] lmtest_0.9-40     tidyselect_1.2.1  htmltools_0.5.8.1 nlme_3.1-168      [53] compiler_4.5.0"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"introduction","dir":"Articles > Prebuilt","previous_headings":"","what":"Introduction","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package, also considering influence covariates like soil pH different degradation parameters. case studies, SFORB parameterisation biexponential decline shown advantages DFOP parameterisation, SFORB included list tested models well. mkin package used version 1.2.10, contains functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"test-data","dir":"Articles > Prebuilt","previous_headings":"Introduction","what":"Test data","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following tables show covariate data 18 datasets read spreadsheet file. Covariate data Dataset Richmond Dataset Richmond 2 Dataset ERTC Dataset Toulouse Dataset Picket Piece Dataset 721 Dataset 722 Dataset 723 Dataset 724 Dataset 725 Dataset 727 Dataset 728 Dataset 729 Dataset 730 Dataset 731 Dataset 732 Dataset 741 Dataset 742","code":"data_file <- system.file(   \"testdata\", \"mesotrione_soil_efsa_2016.xlsx\", package = \"mkin\") meso_ds <- read_spreadsheet(data_file, parent_only = TRUE) pH <- attr(meso_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(meso_ds)) {   print(     kable(mkin_long_to_wide(meso_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE)) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"separate-evaluations","dir":"Articles > Prebuilt","previous_headings":"","what":"Separate evaluations","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"order obtain suitable starting parameters NLHM fits, separate fits five models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. tables , OK indicates convergence C indicates failure converge. separate fits constant variance converged, exception two FOMC fits, one SFORB fit one HS fit. two-component error model, set fits converge larger, convergence problems appearing number non-SFO fits.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\") f_sep_const <- mmkin(   deg_mods,   meso_ds,   error_model = \"const\",   cluster = cl,   quiet = TRUE) status(f_sep_const[, 1:5]) |> kable() status(f_sep_const[, 6:18]) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc[, 1:5]) |> kable() status(f_sep_tc[, 6:18]) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hierarchical-model-fits-without-covariate-effect","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchical model fits without covariate effect","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following code fits hierarchical kinetic models ten combinations five different degradation models two different error models parallel. fits terminate without errors (status OK). model comparisons show fits constant variance consistently preferable corresponding fits two-component error data. confirmed fact parameter b.1 (relative standard deviation fits obtained saemix package), ill-defined fits. obtaining fits well-defined random effects, update set fits, excluding random effects ill-defined according illparms function. updated fits terminate without errors. ill-defined errors remain fits constant variance.","code":"f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) status(f_saem_1) |> kable() anova(f_saem_1) |> kable(digits = 1) illparms(f_saem_1) |> kable() f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hierarchical-model-fits-with-covariate-effect","dir":"Articles > Prebuilt","previous_headings":"","what":"Hierarchical model fits with covariate effect","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following sections, hierarchical fits including model influence pH selected degradation parameters shown parent models. Constant variance selected error model based fits without covariate effects. Random effects ill-defined fits without pH influence excluded. potential influence soil pH included parameters well-defined random effect, experience shown parameters significant pH effect found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"sfo","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"SFO","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"parameter showing pH influence table beta_pH(log_k_meso). confidence interval include zero, indicating influence soil pH log degradation rate constant significantly greater zero. comparison SFO fit without covariate effect confirms considering soil pH improves model, comparison AIC BIC likelihood ratio test.  Endpoints model covariates default calculated median covariate values. quantile can adapted, specific covariate value can given shown .","code":"sfo_pH <- saem(f_sep_const[\"SFO\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_k_meso ~ pH)) summary(sfo_pH)$confint_trans |> kable(digits = 2) anova(f_saem_2[[\"SFO\", \"const\"]], sfo_pH, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"SFO\", \"const\"]]    4 797.56 801.12 -394.78                          sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(sfo_pH) endpoints(sfo_pH) $covariates       pH 50% 5.75  $distimes          DT50     DT90 meso 18.52069 61.52441 endpoints(sfo_pH, covariate_quantile = 0.9) $covariates       pH 90% 7.13  $distimes          DT50     DT90 meso 8.237019 27.36278 endpoints(sfo_pH, covariates = c(pH = 7.0)) $covariates      pH User  7  $distimes         DT50    DT90 meso 8.89035 29.5331"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"fomc","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"FOMC","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"case SFO, confidence interval slope parameter (beta_pH(log_alpha)) quantifying influence soil pH include zero, model comparison clearly indicates model covariate influence preferable. However, random effect alpha well-defined inclusion covariate effect (confidence interval SD.log_alpha includes zero). Therefore, model updated without random effect, ill-defined parameters remain. Model comparison indicates including pH dependence significantly improves fit, reduced model covariate influence results preferable FOMC fit.","code":"fomc_pH <- saem(f_sep_const[\"FOMC\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_alpha ~ pH)) summary(fomc_pH)$confint_trans |> kable(digits = 2) illparms(fomc_pH) [1] \"sd(log_alpha)\" fomc_pH_2 <- update(fomc_pH, no_random_effect = c(\"meso_0\", \"log_alpha\")) illparms(fomc_pH_2) anova(f_saem_2[[\"FOMC\", \"const\"]], fomc_pH, fomc_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"FOMC\", \"const\"]]    5 783.25 787.71 -386.63                          fomc_pH_2                      6 767.49 772.83 -377.75 17.762  1  2.503e-05 *** fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(fomc_pH_2)$confint_trans |> kable(digits = 2) plot(fomc_pH_2) endpoints(fomc_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back meso 17.30248 82.91343 24.95943 endpoints(fomc_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back meso 6.986239 27.02927 8.136621"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"dfop","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"DFOP","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"DFOP fits without covariate effects, random effects two degradation parameters (k2 g) identifiable. fit pH dependent degradation parameters obtained excluding random effects refined DFOP fit without covariate influence, including covariate models two identifiable parameters k2 g. corresponding parameters influence soil pH beta_pH(log_k2) influence soil pH k2, beta_pH(g_qlogis) influence g. Confidence intervals neither include zero, indicating significant difference zero. However, random effect g now ill-defined. fit updated without ill-defined random effect. Now, slope parameter pH effect g ill-defined. Therefore, another attempt made without corresponding covariate model. random effect g ill-defined, fit repeated without . ill-defined parameters remain, model comparison suggests previous model dfop_pH_2 two pH dependent parameters preferable, based information criteria well based likelihood ratio test. focussing parameter identifiability using test confidence interval includes zero, dfop_pH_4 still preferred model. However, kept mind parameter confidence intervals constructed using simple linearisation likelihood. confidence interval random effect g marginally includes zero, suggested acceptable, dfop_pH_2 can considered preferable model.","code":"summary(f_saem_2[[\"DFOP\", \"const\"]])$confint_trans |> kable(digits = 2) dfop_pH <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH)) summary(dfop_pH)$confint_trans |> kable(digits = 2) illparms(dfop_pH) [1] \"sd(g_qlogis)\" dfop_pH_2 <- update(dfop_pH,   no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_2) [1] \"beta_pH(g_qlogis)\" dfop_pH_3 <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH)) illparms(dfop_pH_3) [1] \"sd(g_qlogis)\" dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_4) anova(f_saem_2[[\"DFOP\", \"const\"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik f_saem_2[[\"DFOP\", \"const\"]]    7 782.94 789.18 -384.47 dfop_pH_4                      7 767.35 773.58 -376.68 dfop_pH_2                      8 765.14 772.26 -374.57 dfop_pH_3                      8 769.00 776.12 -376.50 dfop_pH                        9 769.10 777.11 -375.55 anova(dfop_pH_2, dfop_pH_4, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)   dfop_pH_4    7 767.35 773.58 -376.68                        dfop_pH_2    8 765.14 772.26 -374.57 4.2153  1    0.04006 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(dfop_pH_2) endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"sforb","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"SFORB","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"confidence interval beta_pH(log_k_meso_bound_free) includes zero, indicating influence soil pH k_meso_bound_free reliably quantified. Also, confidence interval random effect parameter (SD.log_k_meso_bound_free) includes zero. Using illparms function, ill-defined parameters can found conveniently. remove ill-defined parameters, second variant SFORB model pH influence fitted. ill-defined parameters remain. model comparison SFORB fits includes refined model without covariate effect, versions SFORB fit covariate effect. first model including pH influence preferable based information criteria likelihood ratio test. However, fully identifiable, second model selected.","code":"sforb_pH <- saem(f_sep_const[\"SFORB\", ], no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\"),   covariates = pH,   covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH)) summary(sforb_pH)$confint_trans |> kable(digits = 2) illparms(sforb_pH) [1] \"sd(log_k_meso_bound_free)\"      \"beta_pH(log_k_meso_bound_free)\" sforb_pH_2 <- update(sforb_pH,   no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\", \"log_k_meso_bound_free\"),   covariate_models = list(log_k_meso_free ~ pH)) illparms(sforb_pH_2) anova(f_saem_2[[\"SFORB\", \"const\"]], sforb_pH, sforb_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                               npar    AIC    BIC     Lik   Chisq Df Pr(>Chisq)   f_saem_2[[\"SFORB\", \"const\"]]    7 783.40 789.63 -384.70                         sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0              sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(sforb_pH_2)$confint_trans |> kable(digits = 2) plot(sforb_pH_2) endpoints(sforb_pH_2) $covariates       pH 50% 5.75  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.09735824 0.02631699 0.31602120   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.86549 73.15824 22.02282     7.119554     26.33839 endpoints(sforb_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.13315233 0.03795988 0.61186191   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 7.932495 36.93311 11.11797     5.205671        18.26"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hs","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"HS","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"According output illparms function, random effect break time tb reliably quantified, neither can influence soil pH tb. fit repeated without corresponding covariate model, ill-defined parameters remain. Model comparison confirms model preferable fit without covariate influence, also first version covariate influence.","code":"hs_pH <- saem(f_sep_const[\"HS\", ], no_random_effect = c(\"meso_0\"),   covariates = pH,   covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH)) summary(hs_pH)$confint_trans |> kable(digits = 2) illparms(hs_pH) [1] \"sd(log_tb)\"      \"beta_pH(log_tb)\" hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) illparms(hs_pH_2) anova(f_saem_2[[\"HS\", \"const\"]], hs_pH, hs_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"HS\", \"const\"]]    8 780.08 787.20 -382.04                          hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 *** hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(hs_pH_2)$confint_trans |> kable(digits = 2) plot(hs_pH_2) endpoints(hs_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 14.68725 82.45287 24.82079 14.68725 29.29299 endpoints(hs_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.298536 38.85371 11.69613 8.298536 15.71561"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"comparison-across-parent-models","dir":"Articles > Prebuilt","previous_headings":"Hierarchical model fits with covariate effect","what":"Comparison across parent models","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"model reduction models pH influence, compared . DFOP model pH influence k2 g random effect k2 finally selected best fit. endpoints resulting model listed . Please refer Appendix detailed listing.","code":"anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2) Data: 116 observations of 1 variable(s) grouped in 18 datasets             npar    AIC    BIC     Lik sfo_pH        5 783.09 787.54 -386.54 fomc_pH_2     6 767.49 772.83 -377.75 dfop_pH_4     7 767.35 773.58 -376.68 sforb_pH_2    7 770.94 777.17 -378.47 dfop_pH_2     8 765.14 772.26 -374.57 hs_pH_2      10 766.47 775.37 -373.23 endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"conclusions","dir":"Articles > Prebuilt","previous_headings":"","what":"Conclusions","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"evaluations demonstrate covariate effects can included types parent degradation models. models can refined make fully identifiable.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"session-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Session info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  gridExtra_2.3     jquerylib_0.1.4   systemfonts_1.2.1  [9] scales_1.3.0      textshaping_1.0.0 readxl_1.4.4      yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.9.0       [25] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51         [29] fs_1.6.5          sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     [33] magrittr_2.0.3    digest_0.6.37     grid_4.5.0        mclust_6.1.1      [37] lifecycle_1.0.4   nlme_3.1-168      vctrs_0.6.5       evaluate_1.0.3    [41] glue_1.8.0        cellranger_1.1.0  codetools_0.2-20  ragg_1.3.3        [45] zoo_1.8-13        colorspace_2.1-1  tools_4.5.0       pkgconfig_2.0.3   [49] htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html","id":"hardware-info","dir":"Articles > Prebuilt","previous_headings":"Appendix","what":"Hardware info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"the-data","dir":"Articles > Web_only","previous_headings":"","what":"The data","title":"Example evaluation of FOCUS dataset Z","text":"following code defines example dataset Appendix 7 FOCUS kinetics report (FOCUS Work Group Degradation Kinetics 2014, 354).","code":"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,         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,          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,          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,          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,          25.2, 17.2, 4.8, 4.5, 2.8, 4.4))  FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"parent-and-one-metabolite","dir":"Articles > Web_only","previous_headings":"","what":"Parent and one metabolite","title":"Example evaluation of FOCUS dataset Z","text":"next step set models used kinetic analysis. simultaneous fit parent first metabolite usually straightforward, Step 1 (SFO parent ) skipped . start model 2a, formation decline metabolite Z1 pathway parent directly sink included (default mkin).  obvious parameter summary (component summary), kinetic rate constant parent compound Z sink small t-test parameter suggests significantly different zero. suggests, agreement analysis FOCUS kinetics report, simplify model removing pathway sink. similar result can obtained formation fractions used model formulation:  , ilr transformed formation fraction fitted model takes large value, backtransformed formation fraction parent Z Z1 practically unity. , covariance matrix used calculation confidence intervals returned model overparameterised. simplified model obtained removing pathway sink. following, use parameterisation formation fractions order able compare results FOCUS guidance, makes easier use parameters obtained previous fit adding metabolite.  one transformation product Z0 pathway sink, formation fraction internally fixed unity.","code":"Z.2a <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\"),                 Z1 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.2a) summary(m.Z.2a, 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 ## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762 ## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000 ## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815 Z.2a.ff <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\"),                    Z1 = mkinsub(\"SFO\"),                    use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.2a.ff) summary(m.Z.2a.ff, data = FALSE)$bpar ##            Estimate se_notrans t value     Pr(>t)    Lower    Upper ## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642 ## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600 ## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762 ## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000 ## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815 Z.3 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                Z1 = mkinsub(\"SFO\"), use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.3) summary(m.Z.3, data = FALSE)$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 ## k_Z1   0.48212   0.041727  11.554 4.8268e-12  0.40355   0.5760 ## sigma  4.80411   0.620208   7.746 1.6110e-08  3.52925   6.0790"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"metabolites-z2-and-z3","dir":"Articles > Web_only","previous_headings":"","what":"Metabolites Z2 and Z3","title":"Example evaluation of FOCUS dataset Z","text":"suggested FOCUS report, pathway sink removed metabolite Z1 well next step. step appears questionable basis results, followed purpose comparison. Also, FOCUS report, assumed additional empirical evidence Z1 quickly exclusively hydrolyses Z2.  Finally, metabolite Z3 added model. use optimised differential equation parameter values previous fit order accelerate optimization.  fit corresponds final result chosen Appendix 7 FOCUS report. Confidence intervals returned mkin based internally transformed parameters, however.","code":"Z.5 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                Z2 = mkinsub(\"SFO\"), use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.5) Z.FOCUS <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                    Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                    Z2 = mkinsub(\"SFO\", \"Z3\"),                    Z3 = mkinsub(\"SFO\"),                    use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,                      parms.ini = m.Z.5$bparms.ode,                      quiet = TRUE) ## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : ## Observations with value of zero were removed from the data ## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge: ## false convergence (8) plot_sep(m.Z.FOCUS) summary(m.Z.FOCUS, data = FALSE)$bpar ##             Estimate se_notrans t value     Pr(>t)     Lower      Upper ## Z0_0       96.842440   1.994291 48.5598 4.0226e-42 92.830421 100.854459 ## k_Z0        2.215425   0.118457 18.7023 1.0404e-23  1.989490   2.467019 ## k_Z1        0.478307   0.028257 16.9272 6.2332e-22  0.424709   0.538669 ## k_Z2        0.451642   0.042139 10.7178 1.6304e-14  0.374348   0.544894 ## k_Z3        0.058692   0.015245  3.8499 1.7803e-04  0.034804   0.098975 ## f_Z2_to_Z3  0.471483   0.058348  8.0806 9.6585e-11  0.357720   0.588287 ## sigma       3.984431   0.383402 10.3923 4.5576e-14  3.213126   4.755737 endpoints(m.Z.FOCUS) ## $ff ##   Z2_Z3 Z2_sink  ## 0.47148 0.52852  ##  ## $distimes ##        DT50    DT90 ## Z0  0.31287  1.0393 ## Z1  1.44917  4.8140 ## Z2  1.53473  5.0983 ## Z3 11.80991 39.2317"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"using-the-sforb-model","dir":"Articles > Web_only","previous_headings":"","what":"Using the SFORB model","title":"Example evaluation of FOCUS dataset Z","text":"FOCUS report states, certain tailing time course metabolite Z3. Also, time course parent compound fitted well using SFO model, residues certain low level remain. Therefore, additional model offered , using single first-order reversible binding (SFORB) model metabolite Z3. expected, χ2\\chi^2 error level lower metabolite Z3 using model graphical fit Z3 improved. However, covariance matrix returned.  Therefore, stepwise model building performed starting stage parent two metabolites, starting assumption model fit parent compound can improved using SFORB model.  results much better representation behaviour parent compound Z0. Finally, Z3 added well. models appear overparameterised (covariance matrix returned) sink Z1 left models.  error level fit, especially metabolite Z3, can improved SFORB model chosen metabolite, model capable representing tailing metabolite decline phase.  summary view backtransformed parameters shows get confidence intervals due overparameterisation. optimized excessively small, seems reasonable fix zero.  expected, residual plots Z0 Z3 random case SFO model shown . conclusion, model proposed best-fit model dataset Appendix 7 FOCUS report. graphical representation confidence intervals can finally obtained.  endpoints obtained model clear degradation rate Z3 towards end experiment low DT50_Z3_b2 (second Eigenvalue system two differential equations representing SFORB system Z3, corresponding slower rate constant DFOP model) reported infinity. However, appears feature data.","code":"Z.mkin.1 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFORB\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations ## with value of zero were removed from the data plot_sep(m.Z.mkin.1) summary(m.Z.mkin.1, data = FALSE)$cov.unscaled ## NULL Z.mkin.3 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations ## with value of zero were removed from the data plot_sep(m.Z.mkin.3) Z.mkin.4 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,                       parms.ini = m.Z.mkin.3$bparms.ode,                       quiet = TRUE) ## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = ## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the ## data plot_sep(m.Z.mkin.4) Z.mkin.5 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFORB\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,                       parms.ini = m.Z.mkin.4$bparms.ode[1:4],                       quiet = TRUE) ## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = ## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from ## the data plot_sep(m.Z.mkin.5) m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,                        parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],                                      k_Z3_bound_free = 0),                        fixed_parms = \"k_Z3_bound_free\",                        quiet = TRUE) ## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = ## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed ## from the data plot_sep(m.Z.mkin.5a) mkinparplot(m.Z.mkin.5a) endpoints(m.Z.mkin.5a) ## $ff ## Z0_free   Z2_Z3 Z2_sink Z3_free  ## 1.00000 0.53656 0.46344 1.00000  ##  ## $SFORB ##     Z0_b1     Z0_b2      Z0_g     Z3_b1     Z3_b2      Z3_g  ## 2.4471342 0.0075124 0.9519866 0.0800071 0.0000000 0.9347816  ##  ## $distimes ##      DT50   DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 ## Z0 0.3043 1.1848  0.35666    0.28325     92.267         NA         NA ## Z1 1.5148 5.0320       NA         NA         NA         NA         NA ## Z2 1.6414 5.4526       NA         NA         NA         NA         NA ## Z3     NA     NA       NA         NA         NA     8.6636        Inf"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"document, example evaluations provided Attachment 1 SOP US EPA using NAFTA guidance (US EPA 2015) repeated using mkin. original evaluations reported attachment performed using PestDF version 0.8.4. Note PestDF 0.8.13 version distributed US EPA website today (2019-02-26). datasets now distributed mkin package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"examples-where-dfop-did-not-converge-with-pestdf-0-8-4","dir":"Articles > Web_only","previous_headings":"","what":"Examples where DFOP did not converge with PestDF 0.8.4","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"attachment 1, reported DFOP model converge datasets PestDF 0.8.4 used. four datasets, DFOP model can fitted mkin (see ). negative half-life given PestDF 0.8.4 fits appears result bug. results two models (SFO IORE) .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-5-upper-panel","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 5, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p5a) ## Sums of squares: ##       SFO      IORE      DFOP  ## 465.21753  56.27506  32.06401  ##  ## Critical sum of squares for checking the SFO model: ## [1] 64.4304 ##  ## Parameters: ## $SFO ##          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 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 ## g        6.47e-01 3.67e-06  0.6248   0.6677 ## sigma    1.27e+00 8.91e-06  0.8395   1.6929 ##  ##  ## DTx values: ##      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 ##  ## Representative half-life: ## [1] 321.51"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-5-lower-panel","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 5, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p5b) ## Sums of squares: ##      SFO     IORE     DFOP  ## 94.81123 10.10936  7.55871  ##  ## Critical sum of squares for checking the SFO model: ## [1] 11.77879 ##  ## Parameters: ## $SFO ##          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 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       9.07e-12 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 ##  ##  ## DTx values: ##      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 1.25e+11 7.64e+10 ##  ## Representative half-life: ## [1] 215.87"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-6","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 6","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p6) ## Sums of squares: ##       SFO      IORE      DFOP  ## 188.45361  51.00699  42.46931  ##  ## Critical sum of squares for checking the SFO model: ## [1] 58.39888 ##  ## Parameters: ## $SFO ##          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  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 ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1       2.55e-02 7.33e-06  0.0233  0.0278 ## k2       3.84e-11 5.00e-01  0.0000     Inf ## g        8.61e-01 7.55e-06  0.8314  0.8867 ## sigma    1.46e+00 6.93e-06  0.9661  1.9483 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 ## DFOP 34.1 8.50e+09 1.80e+10 ##  ## Representative half-life: ## [1] 53.17"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-7","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 7","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"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) print(p7) ## Sums of squares: ##      SFO     IORE     DFOP  ## 3661.661 3195.030 3174.145  ##  ## Critical sum of squares for checking the SFO model: ## [1] 3334.194 ##  ## Parameters: ## $SFO ##          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 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       3.62e-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 ##  ##  ## DTx values: ##      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 3.79e+09 1.92e+09 ##  ## Representative half-life: ## [1] 454.55"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-8","dir":"Articles > Web_only","previous_headings":"Examples where the representative half-life deviates from the observed DT50","what":"Example on page 8","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"dataset, IORE fit converge default starting values used mkin IORE model used. Therefore, lower value rate constant used .","code":"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  ## 1996.9408  444.9237  547.5616  ##  ## Critical sum of squares for checking the SFO model: ## [1] 477.4924 ##  ## Parameters: ## $SFO ##          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 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 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: ##      DT50 DT90 DT50_rep ## SFO  86.3  287     86.3 ## IORE 53.4  668    201.0 ## DFOP 55.6  517    253.0 ##  ## Representative half-life: ## [1] 201.03"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-9-upper-panel","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 9, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"example, residuals SFO indicate lack fit model, even abiotic experiment, data suggest simple exponential decline.","code":"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) print(p9a) ## Sums of squares: ##       SFO      IORE      DFOP  ## 839.35238  88.57064   9.93363  ##  ## Critical sum of squares for checking the SFO model: ## [1] 105.5678 ##  ## Parameters: ## $SFO ##          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 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       9.02e-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 ##  ##  ## DTx values: ##      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 1.38e+12 7.68e+11 ##  ## Representative half-life: ## [1] 101.43"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-9-lower-panel","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 9, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":", mkin gives longer slow DT50 DFOP model (17.8 days) PestDF (13.5 days). Presumably, related fact PestDF gives negative value proportion fast degradation 0 1, inclusive. parameter called f PestDF g mkin. mkin, restricted interval 0 1.","code":"p9b <- nafta(NAFTA_SOP_Attachment[[\"p9b\"]]) ## 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  ## 35.64867 23.22334 35.64867  ##  ## Critical sum of squares for checking the SFO model: ## [1] 28.54188 ##  ## Parameters: ## $SFO ##          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    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 1.08e-04  0.0266  0.0569 ## k2         0.0389 2.24e-04  0.0255  0.0592 ## g          0.5256 5.00e-01  0.0000  1.0000 ## sigma      1.5957 2.50e-04  0.9135  2.2779 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  17.8 59.2     17.8 ## IORE 18.4 49.2     14.8 ## DFOP 17.8 59.2     17.8 ##  ## Representative half-life: ## [1] 14.8"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-10","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 10","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":", value N given IORE model, data suggests faster decline towards end experiment, appears physically rather unlikely case photolysis study. seems PestDF constrain N values zero, thus slight difference IORE model parameters PestDF mkin.","code":"p10 <- nafta(NAFTA_SOP_Attachment[[\"p10\"]]) ## Warning in sqrt(diag(covar_notrans)): NaNs produced ## 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  ## 899.4089 336.4348 899.4089  ##  ## Critical sum of squares for checking the SFO model: ## [1] 413.4841 ##  ## Parameters: ## $SFO ##          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     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 111.810 ## k1         0.0495 3.04e-03  0.0188   0.131 ## k2         0.0495 4.92e-04  0.0197   0.124 ## g          0.4487      NaN  0.0000   1.000 ## sigma      8.0152 2.50e-04  4.5886  11.442 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  14.0 46.5    14.00 ## IORE 16.4 29.4     8.86 ## DFOP 14.0 46.5    14.00 ##  ## Representative half-life: ## [1] 8.86"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-11","dir":"Articles > Web_only","previous_headings":"The DT50 was not observed during the study","what":"Example on page 11","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"case, DFOP fit reported PestDF resulted negative value slower rate constant, possible mkin. results agreement.","code":"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) print(p11) ## Sums of squares: ##      SFO     IORE     DFOP  ## 579.6805 204.7932 144.7783  ##  ## Critical sum of squares for checking the SFO model: ## [1] 251.6944 ##  ## Parameters: ## $SFO ##          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 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       9.94e-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 ##  ##  ## DTx values: ##          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 3.07e+11 1.93e+12 6.98e+11 ##  ## Representative half-life: ## [1] 41148169"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant","dir":"Articles > Web_only","previous_headings":"","what":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"following three examples, results obtained mkin reported PestDF. case page 10, N values 1 deemed unrealistic appear result overparameterisation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-12-upper-panel","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 12, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p12a <- nafta(NAFTA_SOP_Attachment[[\"p12a\"]]) ## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix ## Warning in sqrt(diag(covar_notrans)): NaNs produced ## 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  ## 695.4440 220.0685 695.4440  ##  ## Critical sum of squares for checking the SFO model: ## [1] 270.4679 ##  ## Parameters: ## $SFO ##          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    2.436     NA    NA    NA ## N_parent          0.263     NA    NA    NA ## sigma             3.965     NA    NA    NA ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0  100.521 2.74e-10 92.2366 108.805 ## k1          0.124 2.53e-05  0.0908   0.170 ## k2          0.124 2.52e-02  0.0456   0.339 ## g           0.793      NaN  0.0000   1.000 ## sigma       7.048 2.50e-04  4.0349  10.061 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  5.58 18.5     5.58 ## IORE 6.49 13.2     3.99 ## DFOP 5.58 18.5     5.58 ##  ## Representative half-life: ## [1] 3.99"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-12-lower-panel","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 12, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p12b <- nafta(NAFTA_SOP_Attachment[[\"p12b\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in qt(alpha/2, rdf): NaNs produced ## Warning in qt(1 - alpha/2, rdf): NaNs produced ## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## 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  ## 58.90242 19.06353 58.90242  ##  ## Critical sum of squares for checking the SFO model: ## [1] 51.51756 ##  ## Parameters: ## $SFO ##          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    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          0.6473    NaN    NA    NA ## sigma      3.4323    NaN   NaN   NaN ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  11.8 39.1    11.80 ## IORE 12.9 31.4     9.46 ## DFOP 11.8 39.1    11.80 ##  ## Representative half-life: ## [1] 9.46"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-13","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 13","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p13 <- nafta(NAFTA_SOP_Attachment[[\"p13\"]]) ## 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  ## 174.5971 142.3951 174.5971  ##  ## Critical sum of squares for checking the SFO model: ## [1] 172.131 ##  ## Parameters: ## $SFO ##          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   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 ##  ## $DFOP ##          Estimate Pr(>t)    Lower    Upper ## parent_0 92.73500     NA 8.95e+01 95.92118 ## k1        0.00258     NA 4.18e-04  0.01592 ## k2        0.00258     NA 1.75e-03  0.00381 ## g         0.16452     NA 0.00e+00  1.00000 ## sigma     3.41172     NA 2.02e+00  4.79960 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO   269  892      269 ## IORE  261  560      169 ## DFOP  269  892      269 ##  ## Representative half-life: ## [1] 168.51"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf","dir":"Articles > Web_only","previous_headings":"","what":"DT50 not observed in the study and DFOP problems in PestDF","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"slower rate constant reported PestDF negative, physically realistic, possible mkin. fits give results mkin PestDF.","code":"p14 <- nafta(NAFTA_SOP_Attachment[[\"p14\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## 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  ## 48.43249 28.67746 27.26248  ##  ## Critical sum of squares for checking the SFO model: ## [1] 32.83337 ##  ## Parameters: ## $SFO ##          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 9.44e-08     NA   NaN   NaN ## N_parent       3.31e+00     NA   NaN   NaN ## sigma          1.20e+00     NA 0.796   1.6 ##  ## $DFOP ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1       9.53e-03 1.20e-01  0.00638   0.0143 ## k2       5.21e-12 5.00e-01  0.00000      Inf ## g        3.98e-01 2.19e-01  0.30481   0.4998 ## sigma    1.17e+00 7.68e-06  0.77406   1.5610 ##  ##  ## DTx values: ##          DT50     DT90 DT50_rep ## SFO  2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 ## DFOP 3.55e+10 3.44e+11 1.33e+11 ##  ## Representative half-life: ## [1] 6697.44"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero","dir":"Articles > Web_only","previous_headings":"","what":"N is less than 1 and DFOP fraction parameter is below zero","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"mkin, IORE fit affected (deemed unrealistic), fraction parameter DFOP model restricted interval 0 1 mkin. SFO fits give results mkin PestDF.","code":"p15a <- nafta(NAFTA_SOP_Attachment[[\"p15a\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## 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  ## 245.5248 135.0132 245.5248  ##  ## Critical sum of squares for checking the SFO model: ## [1] 165.9335 ##  ## Parameters: ## $SFO ##          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    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 6.28e-02  0.00260   0.0349 ## k2        0.00952 1.27e-04  0.00652   0.0139 ## g         0.21241 5.00e-01       NA       NA ## sigma     4.18778 2.50e-04  2.39747   5.9781 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  72.8  242     72.8 ## IORE 76.3  137     41.3 ## DFOP 72.8  242     72.8 ##  ## Representative half-life: ## [1] 41.33 p15b <- nafta(NAFTA_SOP_Attachment[[\"p15b\"]]) ## 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(p15b) print(p15b) ## Sums of squares: ##       SFO      IORE      DFOP  ## 106.91629  68.55574 106.91629  ##  ## Critical sum of squares for checking the SFO model: ## [1] 84.25618 ##  ## Parameters: ## $SFO ##          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 97.51348 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    NA    NA ## k1       4.86e-03     NA    NA    NA ## k2       4.86e-03     NA    NA    NA ## g        1.88e-01     NA    NA    NA ## sigma    2.76e+00     NA    NA    NA ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO   143  474    143.0 ## IORE  131  236     71.2 ## DFOP  143  474    143.0 ##  ## Representative half-life: ## [1] 71.18"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"the-dfop-fraction-parameter-is-greater-than-1","dir":"Articles > Web_only","previous_headings":"","what":"The DFOP fraction parameter is greater than 1","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"PestDF, DFOP fit seems stuck local minimum, mkin finds solution much lower χ2\\chi^2 error level. half-life slower rate constant DFOP model larger IORE derived half-life, NAFTA recommendation obtained mkin use DFOP representative half-life 8.9 days.","code":"p16 <- nafta(NAFTA_SOP_Attachment[[\"p16\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## 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) print(p16) ## Sums of squares: ##      SFO     IORE     DFOP  ## 3831.804 2062.008 1550.980  ##  ## Critical sum of squares for checking the SFO model: ## [1] 2247.348 ##  ## Parameters: ## $SFO ##          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 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        18.8461 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 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  4.35 14.4     4.35 ## IORE 1.48 32.1     9.67 ## DFOP 0.67 21.4     8.93 ##  ## Representative half-life: ## [1] 8.93"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"results obtained mkin deviate results obtained PestDF either cases one interpretive rules apply, .e. IORE parameter N less one DFOP k values obtained PestDF equal SFO k values, cases DFOP model converge, often lead negative rate constants returned PestDF. Therefore, mkin appears suitable kinetic evaluations according NAFTA guidance.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"test-cases","dir":"Articles > Web_only","previous_headings":"","what":"Test cases","title":"Benchmark timings for mkin","text":"Parent : One metabolite: Two metabolites, synthetic data:","code":"FOCUS_C <- FOCUS_2006_C FOCUS_D <- subset(FOCUS_2006_D, value != 0) parent_datasets <- list(FOCUS_C, FOCUS_D)   t1 <- system.time(mmkin_bench(c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\"), parent_datasets))[[\"elapsed\"]] t2 <- system.time(mmkin_bench(c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\"), parent_datasets,     error_model = \"tc\"))[[\"elapsed\"]] SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) FOMC_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"),   m1 = mkinsub(\"SFO\")) DFOP_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"), # erroneously used FOMC twice, not fixed for consistency   m1 = mkinsub(\"SFO\")) t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[[\"elapsed\"]] t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),     error_model = \"tc\"))[[\"elapsed\"]] t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),     error_model = \"obs\"))[[\"elapsed\"]] m_synth_SFO_lin <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                            M1 = mkinsub(\"SFO\", \"M2\"),                            M2 = mkinsub(\"SFO\"),                            use_of_ff = \"max\", quiet = TRUE)  m_synth_DFOP_par <- mkinmod(parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),                            M1 = mkinsub(\"SFO\"),                            M2 = mkinsub(\"SFO\"),                            use_of_ff = \"max\", quiet = TRUE)  SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data  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\"]]  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\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"results","dir":"Articles > Web_only","previous_headings":"","what":"Results","title":"Benchmark timings for mkin","text":"Benchmarks available error models shown. intended improving mkin, comparing CPUs operating systems. trademarks belong respective owners.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"parent-only","dir":"Articles > Web_only","previous_headings":"Results","what":"Parent only","title":"Benchmark timings for mkin","text":"Constant variance (t1) two-component error model (t2) four models fitted two datasets, .e. eight fits test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"one-metabolite","dir":"Articles > Web_only","previous_headings":"Results","what":"One metabolite","title":"Benchmark timings for mkin","text":"Constant variance (t3), two-component error model (t4), variance variable (t5) three models fitted one dataset, .e. three fits test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"two-metabolites","dir":"Articles > Web_only","previous_headings":"Results","what":"Two metabolites","title":"Benchmark timings for mkin","text":"Constant variance (t6 t7), two-component error model (t8 t9), variance variable (t10 t11) one model fitted one dataset, .e. one fit test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"how-to-benefit-from-compiled-models","dir":"Articles > Web_only","previous_headings":"","what":"How to benefit from compiled models","title":"Performance benefit by using compiled model definitions in mkin","text":"using mkin version equal greater 0.9-36 C compiler available, see message model compiled autogenerated C code defining model using mkinmod. Starting version 0.9.49.9, mkinmod() function checks presence compiler using previous versions, used Sys.(\"gcc\") check. Linux, need essential build tools like make gcc clang installed. Debian based linux distributions, pulled installing build-essential package. MacOS, use personally, reports compiler available default. Windows, need install Rtools path bin directory PATH variable. need modify PATH variable installing Rtools. Instead, recommend put line .Rprofile startup file. just text file R code executed R session starts. named .Rprofile located home directory, generally Documents folder. can check location home directory used R issuing","code":"pkgbuild::has_compiler() Sys.setenv(PATH = paste(\"C:/Rtools/bin\", Sys.getenv(\"PATH\"), sep=\";\")) Sys.getenv(\"HOME\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"comparison-with-other-solution-methods","dir":"Articles > Web_only","previous_headings":"","what":"Comparison with other solution methods","title":"Performance benefit by using compiled model definitions in mkin","text":"First, build simple degradation model parent compound one metabolite, remove zero values dataset. can compare performance Eigenvalue based solution compiled version R implementation differential equations using benchmark package. output code, warnings zero removed FOCUS D dataset suppressed. Since mkin version 0.9.49.11, analytical solution also implemented, included tests . see using compiled model factor 10 faster using deSolve without compiled code.","code":"library(\"mkin\", quietly = TRUE) SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) if (require(rbenchmark)) {   b.1 <- benchmark(     \"deSolve, not compiled\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"deSolve\",        use_compiled = FALSE, quiet = TRUE),     \"Eigenvalue based\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"eigen\", quiet = TRUE),     \"deSolve, compiled\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"deSolve\", quiet = TRUE),     \"analytical\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"analytical\",        use_compiled = FALSE, quiet = TRUE),     replications = 1, order = \"relative\",     columns = c(\"test\", \"replications\", \"relative\", \"elapsed\"))   print(b.1) } else {   print(\"R package rbenchmark is not available\") } ##                    test replications relative elapsed ## 4            analytical            1    1.000   0.111 ## 3     deSolve, compiled            1    1.270   0.141 ## 2      Eigenvalue based            1    1.658   0.184 ## 1 deSolve, not compiled            1   21.973   2.439"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"model-without-analytical-solution","dir":"Articles > Web_only","previous_headings":"","what":"Model without analytical solution","title":"Performance benefit by using compiled model definitions in mkin","text":"evaluation also taken example section mkinfit. analytical solution available system, now Eigenvalue based solution possible, deSolve using without compiled code available. get performance benefit factor 24 using version differential equation model compiled C code! vignette built mkin 1.2.10 ","code":"if (require(rbenchmark)) {   FOMC_SFO <- mkinmod(     parent = mkinsub(\"FOMC\", \"m1\"),     m1 = mkinsub( \"SFO\"))    b.2 <- benchmark(     \"deSolve, not compiled\" = mkinfit(FOMC_SFO, FOCUS_D,                                       use_compiled = FALSE, quiet = TRUE),     \"deSolve, compiled\" = mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE),     replications = 1, order = \"relative\",     columns = c(\"test\", \"replications\", \"relative\", \"elapsed\"))   print(b.2)   factor_FOMC_SFO <- round(b.2[\"1\", \"relative\"]) } else {   factor_FOMC_SFO <- NA   print(\"R package benchmark is not available\") } ## Temporary DLL for differentials generated and loaded ##                    test replications relative elapsed ## 2     deSolve, compiled            1    1.000   0.182 ## 1 deSolve, not compiled            1   23.808   4.333 ## R version 4.5.0 (2025-04-11) ## Platform: x86_64-pc-linux-gnu ## Running under: Debian GNU/Linux 12 (bookworm) ## CPU model: AMD Ryzen 9 7950X 16-Core Processor"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Example evaluations of the dimethenamid data from 2018","text":"first analysis data analysed presented recent journal article nonlinear mixed-effects models degradation kinetics (Ranke et al. 2021). analysis based nlme package development version saemix package unpublished time. Meanwhile, version 3.0 saemix package available CRAN repository. Also, turned error handling Borstel data mkin package time, leading duplication data points soil. dataset mkin package corrected, interface saemix mkin package updated use released version. vignette intended present date analysis data, using corrected dataset released versions mkin saemix.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"data","dir":"Articles > Web_only","previous_headings":"","what":"Data","title":"Example evaluations of the dimethenamid data from 2018","text":"Residue data forming basis endpoints derived conclusion peer review pesticide risk assessment dimethenamid-P published European Food Safety Authority (EFSA) 2018 (EFSA 2018) transcribed risk assessment report (Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018) can downloaded Open EFSA repository https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716. data available mkin package. following code (hidden default, please use button right show ) treats data available racemic mixture dimethenamid (DMTA) enantiomer dimethenamid-P (DMTAP) way, difference degradation behaviour identified EU risk assessment. observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters. Also, datasets observed soil merged, resulting dimethenamid (DMTA) data six soils.","code":"library(mkin, quietly = TRUE) dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"parent-degradation","dir":"Articles > Web_only","previous_headings":"","what":"Parent degradation","title":"Example evaluations of the dimethenamid data from 2018","text":"evaluate observed degradation parent compound using simple exponential decline (SFO) biexponential decline (DFOP), using constant variance (const) two-component variance (tc) error models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Separate evaluations","title":"Example evaluations of the dimethenamid data from 2018","text":"first step, get visual impression fit different models, separate evaluations soil using mmkin function mkin package: plot individual SFO fits shown suggests least datasets degradation slows towards later time points, scatter residuals error smaller smaller values (panel right):  Using biexponential decline (DFOP) results slightly random scatter residuals:  population curve (bold line) plot results taking mean individual transformed parameters, .e. log k1 log k2, well logit g parameter DFOP model). , procedure result parameters represent degradation well, datasets fitted value k2 extremely close zero, leading log k2 value dominates average. alleviated rate constants pass t-test significant difference zero (untransformed scale) considered averaging:  visually much satisfactory, average procedure introduce bias, results individual fits enter population curve weight. nonlinear mixed-effects models can help treating datasets equally fitting parameter distribution model together degradation model error model (see ). remaining trend residuals higher higher predicted residues reduced using two-component error model:  However, note case using error model, fits Flaach BBA 2.3 datasets appear ill-defined, indicated fact converge:","code":"f_parent_mkin_const <- mmkin(c(\"SFO\", \"DFOP\"), dmta_ds,   error_model = \"const\", quiet = TRUE) f_parent_mkin_tc <- mmkin(c(\"SFO\", \"DFOP\"), dmta_ds,   error_model = \"tc\", quiet = TRUE) plot(mixed(f_parent_mkin_const[\"SFO\", ])) plot(mixed(f_parent_mkin_const[\"DFOP\", ])) plot(mixed(f_parent_mkin_const[\"DFOP\", ]), test_log_parms = TRUE) plot(mixed(f_parent_mkin_tc[\"DFOP\", ]), test_log_parms = TRUE) print(f_parent_mkin_tc[\"DFOP\", ]) <mmkin> object Status of individual fits:        dataset model  Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot   DFOP OK    OK      OK     OK      C       OK      C: Optimisation did not converge: iteration limit reached without convergence (10) OK: No warnings"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"nonlinear-mixed-effects-models","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Nonlinear mixed-effects models","title":"Example evaluations of the dimethenamid data from 2018","text":"Instead taking model selection decision individual fits, fit nonlinear mixed-effects models (using different fitting algorithms implemented different packages) model selection using available data time. order make sure decisions unduly influenced type algorithm used, implementation details use wrong control parameters, compare model selection results obtained different R packages, different algorithms checking control parameters.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"nlme","dir":"Articles > Web_only","previous_headings":"Parent degradation > Nonlinear mixed-effects models","what":"nlme","title":"Example evaluations of the dimethenamid data from 2018","text":"nlme package first R extension providing facilities fit nonlinear mixed-effects models. like model selection four combinations degradation models error models based AIC. However, fitting DFOP model constant variance using default control parameters results error, signalling maximum number 50 iterations reached, potentially indicating overparameterisation. Nevertheless, algorithm converges two-component error model used combination DFOP model. can explained fact smaller residues observed later sampling times get weight using two-component error model counteract tendency algorithm try parameter combinations unsuitable fitting data. Note certain degree overparameterisation also indicated warning obtained fitting DFOP two-component error model (‘false convergence’ ‘LME step’ iteration 3). However, warning occur later iterations, specifically last 5 iterations, can ignore warning. model comparison function nlme package can directly applied fits showing much lower AIC DFOP model fitted two-component error model. Also, likelihood ratio test indicates difference significant p-value 0.0001. addition fits, attempts also made include correlations random effects using log Cholesky parameterisation matrix specifying . code used attempts can made visible . SFO variants converge fast, additional parameters introduced lead convergence warnings DFOP model. model comparison clearly show adding correlations random effects improve fits. selected model (DFOP two-component error) fitted data assuming correlations random effects shown .","code":"library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const[\"SFO\", ]) # f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const[\"DFOP\", ]) f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc[\"SFO\", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc[\"DFOP\", ]) anova(   f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc ) Model df    AIC    BIC  logLik   Test L.Ratio p-value f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                        f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998 f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.95 2 vs 3  134.69  <.0001 f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const[\"SFO\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc[\"SFO\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const[\"DFOP\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol) plot(f_parent_nlme_dfop_tc)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"saemix","dir":"Articles > Web_only","previous_headings":"Parent degradation > Nonlinear mixed-effects models","what":"saemix","title":"Example evaluations of the dimethenamid data from 2018","text":"saemix package provided first Open Source implementation Stochastic Approximation Expectation Maximisation (SAEM) algorithm. SAEM fits degradation models can conveniently performed using interface saemix package available current development versions mkin package. corresponding SAEM fits four combinations degradation error models fitted . convergence criterion implemented saemix package, convergence plots need manually checked every fit. define control settings work well parent data fits shown vignette. convergence plot SFO model using constant variance shown .  Obviously selected number iterations sufficient reach convergence. can also said SFO fit using two-component error model.  fitting DFOP model constant variance (see ), parameter convergence unambiguous.  parameters converge credible values, variance k2 (omega2.k2) converges small value. printout saem.mmkin model shows estimated standard deviation k2 across population soils (SD.k2) ill-defined, indicating overparameterisation model. DFOP model fitted two-component error model, also observe estimated variance k2 becomes small, ill-defined, illustrated excessive confidence interval SD.k2.  Doubling number iterations first phase algorithm leads slightly lower likelihood, therefore slightly higher AIC BIC values. even iterations, algorithm stops error message. related variance k2 approximating zero submitted bug saemix package, algorithm converge case. alternative way fit DFOP combination two-component error model use model formulation transformed parameters used per default mkin. using option, convergence slower, eventually algorithm stops well error message. four combinations (SFO/const, SFO/tc, DFOP/const DFOP/tc) version increased iterations can compared using model comparison function saemix package: order check influence likelihood calculation algorithms implemented saemix, likelihood Gaussian quadrature added best fit, AIC values obtained three methods compared. AIC values based importance sampling Gaussian quadrature similar. Using linearisation known less accurate, still gives similar value. order illustrate comparison three method depends degree convergence obtained fit, comparison shown fit using defaults number iterations number MCMC chains. using OpenBlas linear algebra, large difference values obtained Gaussian quadrature, larger number iterations makes lot difference. using LAPACK version coming Debian Bullseye, AIC based Gaussian quadrature almost one obtained methods, also using defaults fit.","code":"library(saemix) saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const[\"SFO\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_sfo_const$so, plot.type = \"convergence\") f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc[\"SFO\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_sfo_tc$so, plot.type = \"convergence\") f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const[\"DFOP\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_dfop_const$so, plot.type = \"convergence\") print(f_parent_saemix_dfop_const) Kinetic nonlinear mixed-effects model fit by SAEM Structural model: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Likelihood computed by importance sampling   AIC BIC logLik   706 704   -344  Fitted parameters:           estimate    lower   upper DMTA_0    97.99583 96.50079 99.4909 k1         0.06377  0.03432  0.0932 k2         0.00848  0.00444  0.0125 g          0.95701  0.91313  1.0009 a.1        1.82141  1.60516  2.0377 SD.DMTA_0  1.64787  0.45729  2.8384 SD.k1      0.57439  0.24731  0.9015 SD.k2      0.03296 -2.50524  2.5712 SD.g       1.10266  0.32354  1.8818 f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ], quiet = TRUE,   control = saemix_control_moreiter, transformations = \"saemix\") plot(f_parent_saemix_dfop_tc$so, plot.type = \"convergence\") print(f_parent_saemix_dfop_tc) Kinetic nonlinear mixed-effects model fit by SAEM Structural model: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Likelihood computed by importance sampling   AIC BIC logLik   666 664   -323  Fitted parameters:           estimate     lower    upper DMTA_0    98.24165  96.29190 100.1914 k1         0.06421   0.03352   0.0949 k2         0.00866   0.00617   0.0111 g          0.95340   0.91218   0.9946 a.1        1.06463   0.87979   1.2495 b.1        0.02964   0.02266   0.0366 SD.DMTA_0  2.03611   0.40361   3.6686 SD.k1      0.59534   0.25692   0.9338 SD.k2      0.00042 -73.00540  73.0062 SD.g       1.04234   0.37189   1.7128 AIC_parent_saemix <- saemix::compare.saemix(   f_parent_saemix_sfo_const$so,   f_parent_saemix_sfo_tc$so,   f_parent_saemix_dfop_const$so,   f_parent_saemix_dfop_tc$so,   f_parent_saemix_dfop_tc_moreiter$so) Likelihoods calculated by importance sampling rownames(AIC_parent_saemix) <- c(   \"SFO const\", \"SFO tc\", \"DFOP const\", \"DFOP tc\", \"DFOP tc more iterations\") print(AIC_parent_saemix) AIC    BIC SFO const               796.38 795.34 SFO tc                  798.38 797.13 DFOP const              705.75 703.88 DFOP tc                 665.67 663.59 DFOP tc more iterations 665.85 663.76 f_parent_saemix_dfop_tc$so <-   saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) AIC_parent_saemix_methods <- c(   is = AIC(f_parent_saemix_dfop_tc$so, method = \"is\"),   gq = AIC(f_parent_saemix_dfop_tc$so, method = \"gq\"),   lin = AIC(f_parent_saemix_dfop_tc$so, method = \"lin\") ) print(AIC_parent_saemix_methods) is     gq    lin  665.67 665.74 665.13 f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ]) f_parent_saemix_dfop_tc_defaults$so <-   saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so) AIC_parent_saemix_methods_defaults <- c(   is = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"is\"),   gq = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"gq\"),   lin = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"lin\") ) print(AIC_parent_saemix_methods_defaults) is     gq    lin  670.09 669.37 671.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"comparison","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Comparison","title":"Example evaluations of the dimethenamid data from 2018","text":"following table gives AIC values obtained backend packages using control parameters (800 iterations burn-, 300 iterations second phase, 15 chains).","code":"AIC_all <- data.frame(   check.names = FALSE,   \"Degradation model\" = c(\"SFO\", \"SFO\", \"DFOP\", \"DFOP\"),   \"Error model\" = c(\"const\", \"tc\", \"const\", \"tc\"),   nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),   saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = \"lin\"),   saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = \"is\") ) kable(AIC_all)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"conclusion","dir":"Articles > Web_only","previous_headings":"","what":"Conclusion","title":"Example evaluations of the dimethenamid data from 2018","text":"detailed analysis dimethenamid dataset confirmed DFOP model provides appropriate description decline parent compound data. hand, closer inspection results revealed variability k2 parameter across population soils ill-defined. coincides observation parameter robustly quantified soils. Regarding regulatory use data, claimed improved characterisation mean parameter values across population obtained using nonlinear mixed-effects models presented . However, attempts quantify variability slower rate constant biphasic decline dimethenamid indicate data sufficient characterise variability satisfactory precision.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"","what":"Session Info","title":"Example evaluations of the dimethenamid data from 2018","text":"","code":"sessionInfo() R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] stats     graphics  grDevices utils     datasets  methods   base       other attached packages: [1] saemix_3.3   npde_3.5     nlme_3.1-168 mkin_1.2.10  knitr_1.49    loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  parallel_4.5.0    gridExtra_2.3     jquerylib_0.1.4    [9] systemfonts_1.2.1 scales_1.3.0      textshaping_1.0.0 yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.9.0       [25] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51         [29] fs_1.6.5          sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     [33] magrittr_2.0.3    digest_0.6.37     grid_4.5.0        mclust_6.1.1      [37] lifecycle_1.0.4   vctrs_0.6.5       evaluate_1.0.3    glue_1.8.0        [41] codetools_0.2-20  ragg_1.3.3        zoo_1.8-13        colorspace_2.1-1  [45] rmarkdown_2.29    tools_4.5.0       pkgconfig_2.0.3   htmltools_0.5.8.1"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package, also considering influence covariates like soil pH different degradation parameters. case studies, SFORB parameterisation biexponential decline shown advantages DFOP parameterisation, SFORB included list tested models well. mkin package used version 1.2.10, contains functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"Introduction","what":"Test data","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following tables show covariate data 18 datasets read spreadsheet file. Covariate data Dataset Richmond Dataset Richmond 2 Dataset ERTC Dataset Toulouse Dataset Picket Piece Dataset 721 Dataset 722 Dataset 723 Dataset 724 Dataset 725 Dataset 727 Dataset 728 Dataset 729 Dataset 730 Dataset 731 Dataset 732 Dataset 741 Dataset 742","code":"data_file <- system.file(   \"testdata\", \"mesotrione_soil_efsa_2016.xlsx\", package = \"mkin\") meso_ds <- read_spreadsheet(data_file, parent_only = TRUE) pH <- attr(meso_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(meso_ds)) {   print(     kable(mkin_long_to_wide(meso_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE)) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"","what":"Separate evaluations","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"order obtain suitable starting parameters NLHM fits, separate fits five models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. tables , OK indicates convergence C indicates failure converge. separate fits constant variance converged, exception two FOMC fits, one SFORB fit one HS fit. two-component error model, set fits converge larger, convergence problems appearing number non-SFO fits.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\") f_sep_const <- mmkin(   deg_mods,   meso_ds,   error_model = \"const\",   cluster = cl,   quiet = TRUE) status(f_sep_const[, 1:5]) |> kable() status(f_sep_const[, 6:18]) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc[, 1:5]) |> kable() status(f_sep_tc[, 6:18]) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hierarchical-models-without-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models without covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following code fits hierarchical kinetic models ten combinations five different degradation models two different error models parallel. fits terminate without errors (status OK). model comparisons show fits constant variance consistently preferable corresponding fits two-component error data. confirmed fact parameter b.1 (relative standard deviation fits obtained saemix package), ill-defined fits. obtaining fits well-defined random effects, update set fits, excluding random effects degradation parameters ill-defined according illparms function. updated fits terminate without errors, ill-defined ill-defined errors remain fits constant variance.","code":"f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) status(f_saem_1) |> kable() anova(f_saem_1) |> kable(digits = 1) illparms(f_saem_1) |> kable() f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hierarchical-models-with-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models with covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following sections, hierarchical fits including model influence pH selected degradation parameters shown parent models. Constant variance selected error model based fits without covariate effects. Random effects ill-defined fits without pH influence excluded. potential influence soil pH included parameters well-defined random effect, experience shown parameters significant pH effect found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"sfo","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFO","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"parameter showing pH influence table beta_pH(log_k_meso). confidence interval include zero, indicating influence soil pH log degradation rate constant significantly greater zero. comparison SFO fit without covariate effect confirms considering soil pH improves model, comparison AIC BIC likelihood ratio test.  Endpoints model covariates default calculated median covariate values. quantile can adapted, specific covariate value can given shown .","code":"sfo_pH <- saem(f_sep_const[\"SFO\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_k_meso ~ pH), center_covariates = \"median\") summary(sfo_pH)$confint_trans |> kable(digits = 2) anova(f_saem_2[[\"SFO\", \"const\"]], sfo_pH, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"SFO\", \"const\"]]    4 797.56 801.12 -394.78                          sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(sfo_pH) endpoints(sfo_pH) $covariates       pH 50% 5.75  $distimes          DT50     DT90 meso 18.52069 61.52441 endpoints(sfo_pH, covariate_quantile = 0.9) $covariates       pH 90% 7.13  $distimes          DT50     DT90 meso 8.237019 27.36278 endpoints(sfo_pH, covariates = c(pH = 7.0)) $covariates      pH User  7  $distimes         DT50    DT90 meso 8.89035 29.5331"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"fomc","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"FOMC","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"case SFO, confidence interval slope parameter (beta_pH(log_alpha)) quantifying influence soil pH include zero, model comparison clearly indicates model covariate influence preferable. However, random effect alpha well-defined inclusion covariate effect (confidence interval SD.log_alpha includes zero). Therefore, model updated without random effect, ill-defined parameters remain. Model comparison indicates including pH dependence significantly improves fit, reduced model covariate influence results preferable FOMC fit.","code":"fomc_pH <- saem(f_sep_const[\"FOMC\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_alpha ~ pH), center_covariates = \"median\") summary(fomc_pH)$confint_trans |> kable(digits = 2) illparms(fomc_pH) [1] \"sd(log_alpha)\" fomc_pH_2 <- update(fomc_pH, no_random_effect = c(\"meso_0\", \"log_alpha\")) illparms(fomc_pH_2) anova(f_saem_2[[\"FOMC\", \"const\"]], fomc_pH, fomc_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"FOMC\", \"const\"]]    5 783.25 787.71 -386.63                          fomc_pH_2                      6 766.50 771.84 -377.25 18.753  1  1.488e-05 *** fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(fomc_pH_2)$confint_trans |> kable(digits = 2) plot(fomc_pH_2) endpoints(fomc_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back meso 16.86448 83.26704 25.06588 endpoints(fomc_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back meso 7.835768 31.46451  9.47176"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"dfop","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"DFOP","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"DFOP fits without covariate effects, random effects two degradation parameters (k2 g) identifiable. fit pH dependent degradation parameters obtained excluding random effects refined DFOP fit without covariate influence, including covariate models two identifiable parameters k2 g. corresponding parameters influence soil pH beta_pH(log_k2) influence soil pH k2, beta_pH(g_qlogis) influence g. Confidence intervals neither include zero, indicating significant difference zero. However, random effect g now ill-defined. fit updated without ill-defined random effect. Now, slope parameter pH effect g ill-defined. Therefore, another attempt made without corresponding covariate model. random effect g ill-defined, fit repeated without . ill-defined parameters remain, model comparison suggests previous model dfop_pH_2 two pH dependent parameters preferable, based information criteria well based likelihood ratio test. focussing parameter identifiability using test confidence interval includes zero, dfop_pH_4 still preferred model. However, kept mind parameter confidence intervals constructed using simple linearisation likelihood. confidence interval random effect g marginally includes zero, suggested acceptable, dfop_pH_2 can considered preferable model.","code":"summary(f_saem_2[[\"DFOP\", \"const\"]])$confint_trans |> kable(digits = 2) dfop_pH <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH),   center_covariates = \"median\" ) summary(dfop_pH)$confint_trans |> kable(digits = 2) illparms(dfop_pH) [1] \"sd(g_qlogis)\" dfop_pH_2 <- update(dfop_pH,   no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_2) [1] \"beta_pH(g_qlogis)\" dfop_pH_3 <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH),   center_covariates = \"median\") illparms(dfop_pH_3) [1] \"sd(g_qlogis)\" dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_4) anova(f_saem_2[[\"DFOP\", \"const\"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik f_saem_2[[\"DFOP\", \"const\"]]    7 782.94 789.18 -384.47 dfop_pH_4                      7 767.35 773.58 -376.68 dfop_pH_2                      8 765.06 772.18 -374.53 dfop_pH_3                      8 769.00 776.12 -376.50 dfop_pH                        9 769.10 777.11 -375.55 anova(dfop_pH_2, dfop_pH_4, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)   dfop_pH_4    7 767.35 773.58 -376.68                        dfop_pH_2    8 765.06 772.18 -374.53 4.2909  1    0.03832 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(dfop_pH_2) endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.46687 74.91602 22.55197 4.568623 24.64483 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.370528 28.37824 8.542701 4.568623 8.785409"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"sforb","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFORB","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"confidence interval beta_pH(log_k_meso_bound_free) includes zero, indicating influence soil pH k_meso_bound_free reliably quantified. Also, confidence interval random effect parameter (SD.log_k_meso_bound_free) includes zero. Using illparms function, ill-defined parameters can found conveniently. remove ill-defined parameters, second variant SFORB model pH influence fitted. ill-defined parameters remain. model comparison SFORB fits includes refined model without covariate effect, versions SFORB fit covariate effect. first model including pH influence preferable based information criteria likelihood ratio test. However, fully identifiable, second model selected.","code":"sforb_pH <- saem(f_sep_const[\"SFORB\", ], no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\"),   covariates = pH,   covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH),   center_covariates = \"median\") summary(sforb_pH)$confint_trans |> kable(digits = 2) illparms(sforb_pH) [1] \"sd(log_k_meso_bound_free)\"      \"beta_pH(log_k_meso_bound_free)\" sforb_pH_2 <- update(sforb_pH,   no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\", \"log_k_meso_bound_free\"),   covariate_models = list(log_k_meso_free ~ pH)) illparms(sforb_pH_2) anova(f_saem_2[[\"SFORB\", \"const\"]], sforb_pH, sforb_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                               npar    AIC    BIC     Lik   Chisq Df Pr(>Chisq)   f_saem_2[[\"SFORB\", \"const\"]]    7 783.40 789.63 -384.70                         sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0              sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(sforb_pH_2)$confint_trans |> kable(digits = 2) plot(sforb_pH_2) endpoints(sforb_pH_2) $covariates       pH 50% 5.75  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.09735824 0.02631699 0.31602120   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.86549 73.15824 22.02282     7.119554     26.33839 endpoints(sforb_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.13315233 0.03795988 0.61186191   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 7.932495 36.93311 11.11797     5.205671        18.26"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hs","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"HS","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"According output illparms function, random effect break time tb reliably quantified, neither can influence soil pH tb. fit repeated without corresponding covariate model, ill-defined parameters remain. Model comparison confirms model preferable fit without covariate influence, also first version covariate influence.","code":"hs_pH <- saem(f_sep_const[\"HS\", ], no_random_effect = c(\"meso_0\"),   covariates = pH,   covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH),   center_covariates = \"median\") summary(hs_pH)$confint_trans |> kable(digits = 2) illparms(hs_pH) [1] \"sd(log_tb)\"      \"beta_pH(log_tb)\" hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) illparms(hs_pH_2) anova(f_saem_2[[\"HS\", \"const\"]], hs_pH, hs_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"HS\", \"const\"]]    8 780.08 787.20 -382.04                          hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 *** hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(hs_pH_2)$confint_trans |> kable(digits = 2) plot(hs_pH_2) endpoints(hs_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 14.68725 82.45287 24.82079 14.68725 29.29299 endpoints(hs_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.298536 38.85371 11.69613 8.298536 15.71561"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"comparison-across-parent-models","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"Comparison across parent models","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"model reduction models pH influence, compared . DFOP model pH influence k2 g random effect k2 finally selected best fit. endpoints resulting model listed . Please refer Appendix detailed listing.","code":"anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2) Data: 116 observations of 1 variable(s) grouped in 18 datasets             npar    AIC    BIC     Lik sfo_pH        5 783.09 787.54 -386.54 fomc_pH_2     6 766.50 771.84 -377.25 dfop_pH_4     7 767.35 773.58 -376.68 sforb_pH_2    7 770.94 777.17 -378.47 dfop_pH_2     8 765.06 772.18 -374.53 hs_pH_2      10 766.47 775.37 -373.23 endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.46687 74.91602 22.55197 4.568623 24.64483 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.370528 28.37824 8.542701 4.568623 8.785409"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"evaluations demonstrate covariate effects can included types parent degradation models. models can refined make fully identifiable.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"fits-without-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits without covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchical SFO fit constant variance  Hierarchical FOMC fit constant variance  Hierarchical DFOP fit constant variance  Hierarchical SFORB fit constant variance  Hierarchical HS fit constant variance","code":"saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:01 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.602 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling   AIC   BIC logLik   800 804.5   -395  Optimised parameters:                  est.    lower   upper meso_0        92.0705  89.9917 94.1493 log_k_meso    -3.1641  -3.4286 -2.8996 a.1            5.4628   4.6421  6.2835 SD.meso_0      0.0611 -98.3545 98.4767 SD.log_k_meso  0.5616   0.3734  0.7499  Correlation:             meso_0 log_k_meso 0.1132  Random effects:                 est.    lower   upper SD.meso_0     0.0611 -98.3545 98.4767 SD.log_k_meso 0.5616   0.3734  0.7499  Variance model:      est. lower upper a.1 5.463 4.642 6.284  Backtransformed parameters:            est.    lower    upper meso_0 92.07053 89.99172 94.14933 k_meso  0.04225  0.03243  0.05505  Estimated disappearance times:       DT50 DT90 meso 16.41 54.5 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:02 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.983 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 793.6 -386.7  Optimised parameters:                 est.     lower   upper meso_0       93.5648  91.42864 95.7009 log_alpha     0.7645   0.28068  1.2484 log_beta      3.6597   3.05999  4.2594 a.1           5.0708   4.29823  5.8435 SD.meso_0     0.1691 -34.01517 34.3535 SD.log_alpha  0.3764   0.05834  0.6945 SD.log_beta   0.3903  -0.06074  0.8414  Correlation:            meso_0  log_lph log_alpha -0.2839         log_beta  -0.3443  0.8855  Random effects:                est.     lower   upper SD.meso_0    0.1691 -34.01517 34.3535 SD.log_alpha 0.3764   0.05834  0.6945 SD.log_beta  0.3903  -0.06074  0.8414  Variance model:      est. lower upper a.1 5.071 4.298 5.843  Backtransformed parameters:          est.  lower  upper meso_0 93.565 91.429 95.701 alpha   2.148  1.324  3.485 beta   38.850 21.327 70.770  Estimated disappearance times:      DT50  DT90 DT50back meso 14.8 74.64    22.47 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:02 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.202 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.6 795.6 -384.8  Optimised parameters:                est.     lower   upper meso_0      93.6684  91.63599 95.7008 log_k1      -1.7354  -2.61433 -0.8565 log_k2      -3.4015  -3.73323 -3.0697 g_qlogis    -1.6341  -2.66133 -0.6069 a.1          4.7803   4.01269  5.5479 SD.meso_0    0.1661 -30.97086 31.3031 SD.log_k1    0.1127  -2.59680  2.8223 SD.log_k2    0.6394   0.41499  0.8638 SD.g_qlogis  0.8166   0.09785  1.5353  Correlation:           meso_0  log_k1  log_k2  log_k1    0.1757                 log_k2    0.0199  0.2990         g_qlogis  0.0813 -0.7431 -0.3826  Random effects:               est.     lower   upper SD.meso_0   0.1661 -30.97086 31.3031 SD.log_k1   0.1127  -2.59680  2.8223 SD.log_k2   0.6394   0.41499  0.8638 SD.g_qlogis 0.8166   0.09785  1.5353  Variance model:     est. lower upper a.1 4.78 4.013 5.548  Backtransformed parameters:            est.    lower    upper meso_0 93.66841 91.63599 95.70082 k1      0.17633  0.07322  0.42466 k2      0.03332  0.02392  0.04643 g       0.16327  0.06529  0.35277  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 16.04 63.75    19.19   3.931    20.8 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:02 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.557 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 795.4 -384.7  Optimised parameters:                             est.    lower  upper meso_free_0              93.6285  91.6262 95.631 log_k_meso_free          -2.8314  -3.1375 -2.525 log_k_meso_free_bound    -3.2213  -4.4695 -1.973 log_k_meso_bound_free    -2.4246  -3.5668 -1.282 a.1                       4.7372   3.9542  5.520 SD.meso_free_0            0.1634 -32.7769 33.104 SD.log_k_meso_free        0.4885   0.3080  0.669 SD.log_k_meso_free_bound  0.2876  -1.7955  2.371 SD.log_k_meso_bound_free  0.9942   0.2181  1.770  Correlation:                        ms_fr_0 lg_k_m_ lg_k_ms_f_ log_k_meso_free        0.2332                    log_k_meso_free_bound  0.1100  0.5964            log_k_meso_bound_free -0.0413  0.3697  0.8025     Random effects:                            est.    lower  upper SD.meso_free_0           0.1634 -32.7769 33.104 SD.log_k_meso_free       0.4885   0.3080  0.669 SD.log_k_meso_free_bound 0.2876  -1.7955  2.371 SD.log_k_meso_bound_free 0.9942   0.2181  1.770  Variance model:      est. lower upper a.1 4.737 3.954  5.52  Backtransformed parameters:                       est.    lower    upper meso_free_0       93.62849 91.62622 95.63075 k_meso_free        0.05893  0.04339  0.08004 k_meso_free_bound  0.03990  0.01145  0.13903 k_meso_bound_free  0.08851  0.02825  0.27736  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.15333 0.03402 0.20881   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 14.79 60.81     18.3        4.521        20.37 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:03 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.61 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   781.9 789.9   -382  Optimised parameters:               est.    lower   upper meso_0    93.34242  91.4730 95.2118 log_k1    -2.77312  -3.0826 -2.4637 log_k2    -3.61854  -3.8430 -3.3941 log_tb     2.00266   1.3357  2.6696 a.1        4.47693   3.7059  5.2479 SD.meso_0  0.07963 -63.1661 63.3253 SD.log_k1  0.47817   0.2467  0.7097 SD.log_k2  0.39216   0.2137  0.5706 SD.log_tb  0.94683   0.4208  1.4728  Correlation:         meso_0  log_k1  log_k2  log_k1  0.1627                 log_k2  0.0063 -0.0301         log_tb  0.0083 -0.3931 -0.1225  Random effects:              est.    lower   upper SD.meso_0 0.07963 -63.1661 63.3253 SD.log_k1 0.47817   0.2467  0.7097 SD.log_k2 0.39216   0.2137  0.5706 SD.log_tb 0.94683   0.4208  1.4728  Variance model:      est. lower upper a.1 4.477 3.706 5.248  Backtransformed parameters:            est.    lower    upper meso_0 93.34242 91.47303 95.21181 k1      0.06247  0.04584  0.08512 k2      0.02682  0.02143  0.03357 tb      7.40872  3.80282 14.43376  Estimated disappearance times:      DT50 DT90 DT50back DT50_k1 DT50_k2 meso   16   76    22.88    11.1   25.84"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"fits-with-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits with covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchichal SFO fit pH influence  Hierarchichal FOMC fit pH influence  Refined hierarchichal FOMC fit pH influence  Hierarchichal DFOP fit pH influence  Refined hierarchical DFOP fit pH influence  refined hierarchical DFOP fit pH influence  Hierarchichal SFORB fit pH influence  Refined hierarchichal SFORB fit pH influence  Hierarchichal HS fit pH influence  Refined hierarchichal HS fit pH influence","code":"saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:15 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.924 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   783.1 787.5 -386.5  Optimised parameters:                        est.   lower   upper meso_0              91.3481 89.2688 93.4275 log_k_meso          -3.2854 -3.4590 -3.1118 beta_pH(log_k_meso)  0.5871  0.3684  0.8059 a.1                  5.4750  4.7085  6.2415 SD.log_k_meso        0.3471  0.2258  0.4684  Correlation:                      meso_0  lg_k_ms log_k_meso           0.1797         beta_pH(log_k_meso) -0.0183 -0.2379  Random effects:                 est.  lower  upper SD.log_k_meso 0.3471 0.2258 0.4684  Variance model:      est. lower upper a.1 5.475 4.709 6.242  Backtransformed parameters:            est.    lower    upper meso_0 91.34814 89.26880 93.42748 k_meso  0.03743  0.03146  0.04452  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 meso 18.52 61.52 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:18 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.901 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.1 776.3   -378  Optimised parameters:                         est.      lower   upper meso_0             92.840646  90.750461 94.9308 log_alpha           1.114051   0.475668  1.7524 beta_pH(log_alpha)  0.577505   0.369805  0.7852 log_beta            4.214099   3.438851  4.9893 a.1                 5.027768   4.322028  5.7335 SD.log_alpha        0.004034 -23.766993 23.7751 SD.log_beta         0.374640   0.009252  0.7400  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.3220                 beta_pH(log_alpha) -0.0789  0.1148         log_beta           -0.3544  0.9709  0.1628  Random effects:                  est.      lower upper SD.log_alpha 0.004034 -23.766993 23.78 SD.log_beta  0.374640   0.009252  0.74  Variance model:      est. lower upper a.1 5.028 4.322 5.734  Backtransformed parameters:          est.  lower   upper meso_0 92.841 90.750  94.931 alpha   3.047  1.609   5.769 beta   67.633 31.151 146.840  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back meso 17.28 76.37    22.99 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:21 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.35 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   766.5 771.8 -377.3  Optimised parameters:                       est.   lower   upper meso_0             93.1950 91.1047 95.2853 log_alpha           0.8125  0.3292  1.2957 beta_pH(log_alpha)  0.5497  0.3490  0.7504 log_beta            3.8464  3.2179  4.4750 a.1                 4.9972  4.2976  5.6968 SD.log_beta         0.3829  0.2499  0.5159  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.3094                 beta_pH(log_alpha) -0.0779  0.0841         log_beta           -0.3493  0.9533  0.1434  Random effects:               est.  lower  upper SD.log_beta 0.3829 0.2499 0.5159  Variance model:      est. lower upper a.1 4.997 4.298 5.697  Backtransformed parameters:          est. lower  upper meso_0 93.195 91.10 95.285 alpha   2.253  1.39  3.654 beta   46.826 24.98 87.793  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back meso 16.86 83.27    25.07 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:25 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.592 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.1 777.1 -375.5  Optimised parameters:                        est.   lower    upper meso_0            92.843344 90.8464 94.84028 log_k1            -2.815685 -3.0888 -2.54261 log_k2            -3.956384 -4.4741 -3.43868 beta_pH(log_k2)    1.308417  0.6948  1.92203 g_qlogis          -0.121394 -0.5691  0.32627 beta_pH(g_qlogis) -0.565988 -1.0394 -0.09262 a.1                4.955518  4.2597  5.65135 SD.log_k2          0.758963  0.4685  1.04943 SD.g_qlogis        0.005215 -9.9561  9.96656  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.2706                                 log_k2            -0.0457 -0.0667                         beta_pH(log_k2)    0.0554 -0.1291 -0.5566                 g_qlogis           0.1004 -0.4462 -0.4397  0.5042         beta_pH(g_qlogis)  0.1267  0.4226  0.0123 -0.0438  0.2029  Random effects:                 est.   lower upper SD.log_k2   0.758963  0.4685 1.049 SD.g_qlogis 0.005215 -9.9561 9.967  Variance model:      est. lower upper a.1 4.956  4.26 5.651  Backtransformed parameters:            est.    lower    upper meso_0 92.84334 90.84641 94.84028 k1      0.05986  0.04556  0.07866 k2      0.01913  0.01140  0.03211 g       0.46969  0.36145  0.58085  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 20.23 88.45    26.62   11.58   36.23 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:30 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 4.483 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   765.1 772.2 -374.5  Optimised parameters:                      est.   lower    upper meso_0            93.1612 91.0766 95.24580 log_k1            -1.8857 -2.8975 -0.87395 log_k2            -3.5711 -3.8859 -3.25622 beta_pH(log_k2)    0.8252  0.4952  1.15513 g_qlogis          -1.5326 -2.6994 -0.36574 beta_pH(g_qlogis) -0.8365 -1.7163  0.04333 a.1                4.9218  4.2328  5.61070 SD.log_k2          0.4011  0.2604  0.54184  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.1734                                 log_k2             0.0553  0.6421                         beta_pH(log_k2)   -0.0382 -0.5121 -0.5860                 g_qlogis           0.0616 -0.8501 -0.7251  0.4941         beta_pH(g_qlogis)  0.1408 -0.3240 -0.2716 -0.0041  0.6264  Random effects:             est.  lower  upper SD.log_k2 0.4011 0.2604 0.5418  Variance model:      est. lower upper a.1 4.922 4.233 5.611  Backtransformed parameters:            est.    lower    upper meso_0 93.16118 91.07655 95.24580 k1      0.15172  0.05516  0.41730 k2      0.02813  0.02053  0.03853 g       0.17762  0.06301  0.40957  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 18.47 74.92    22.55   4.569   24.64 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:37 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.213 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   767.4 773.6 -376.7  Optimised parameters:                    est.   lower   upper meso_0          93.3011 91.1905 95.4118 log_k1          -2.1487 -2.7607 -1.5367 log_k2          -3.6066 -3.9305 -3.2828 beta_pH(log_k2)  0.7821  0.4126  1.1517 g_qlogis        -1.0373 -1.9337 -0.1409 a.1              5.0095  4.3082  5.7108 SD.log_k2        0.4622  0.3009  0.6235  Correlation:                  meso_0  log_k1  log_k2  b_H(_2) log_k1           0.2179                         log_k2           0.0271  0.5067                 beta_pH(log_k2) -0.0326 -0.5546 -0.5485         g_qlogis         0.0237 -0.8479 -0.6866  0.6123  Random effects:             est.  lower  upper SD.log_k2 0.4622 0.3009 0.6235  Variance model:      est. lower upper a.1 5.009 4.308 5.711  Backtransformed parameters:            est.    lower    upper meso_0 93.30113 91.19050 95.41175 k1      0.11664  0.06325  0.21508 k2      0.02714  0.01963  0.03752 g       0.26168  0.12635  0.46483  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 17.09 73.67    22.18   5.943   25.54 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:41 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.758 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   768.8 776.8 -375.4  Optimised parameters:                                   est.   lower   upper meso_free_0                    93.4204 91.3213 95.5195 log_k_meso_free                -2.9408 -3.2344 -2.6471 beta_pH(log_k_meso_free)        0.4232  0.1769  0.6695 log_k_meso_free_bound          -3.4889 -4.9243 -2.0535 log_k_meso_bound_free          -2.9130 -4.3047 -1.5212 beta_pH(log_k_meso_bound_free)  1.2290 -0.2107  2.6687 a.1                             4.9031  4.1795  5.6268 SD.log_k_meso_free              0.3454  0.2252  0.4656 SD.log_k_meso_bound_free        0.1277 -1.9459  2.2012  Correlation:                                 ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_ log_k_meso_free                 0.3460                                        beta_pH(log_k_meso_free)       -0.0930 -0.4206                                log_k_meso_free_bound           0.2439  0.7749 -0.3492                        log_k_meso_bound_free           0.1350  0.6400 -0.2912   0.9346               beta_pH(log_k_meso_bound_free) -0.2216 -0.4778 -0.0111  -0.6566    -0.6498     Random effects:                            est.   lower  upper SD.log_k_meso_free       0.3454  0.2252 0.4656 SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012  Variance model:      est. lower upper a.1 4.903  4.18 5.627  Backtransformed parameters:                       est.     lower    upper meso_free_0       93.42040 91.321340 95.51946 k_meso_free        0.05282  0.039384  0.07085 k_meso_free_bound  0.03054  0.007268  0.12829 k_meso_bound_free  0.05431  0.013505  0.21845  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g   0.1121  0.0256  0.3148   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.42 75.2    22.64        6.185        27.08 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:44 2025  Date of summary: Fri Nov 28 09:15:24 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.971 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.9 777.2 -378.5  Optimised parameters:                             est.   lower   upper meso_free_0              93.3196 91.1650 95.4743 log_k_meso_free          -3.0207 -3.2655 -2.7759 beta_pH(log_k_meso_free)  0.5435  0.3329  0.7542 log_k_meso_free_bound    -3.8001 -5.1809 -2.4193 log_k_meso_bound_free    -2.9462 -4.2411 -1.6513 a.1                       5.0825  4.3709  5.7940 SD.log_k_meso_free        0.3338  0.2175  0.4502  Correlation:                           ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_ log_k_meso_free           0.3556                            beta_pH(log_k_meso_free) -0.0422 -0.2041                    log_k_meso_free_bound     0.2513  0.6866 -0.0400            log_k_meso_bound_free     0.1292  0.5341 -0.0129  0.9214     Random effects:                      est.  lower  upper SD.log_k_meso_free 0.3338 0.2175 0.4502  Variance model:      est. lower upper a.1 5.082 4.371 5.794  Backtransformed parameters:                       est.     lower    upper meso_free_0       93.31965 91.165044 95.47425 k_meso_free        0.04877  0.038177  0.06230 k_meso_free_bound  0.02237  0.005623  0.08898 k_meso_bound_free  0.05254  0.014392  0.19179  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.09736 0.02632 0.31602   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.87 73.16    22.02         7.12        26.34 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:48 2025  Date of summary: Fri Nov 28 09:15:25 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.254 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.8 779.6 -373.9  Optimised parameters:                     est.   lower   upper meso_0          93.32599 91.4658 95.1862 log_k1          -3.08497 -3.2830 -2.8870 beta_pH(log_k1)  0.47472  0.2334  0.7160 log_k2          -3.68267 -3.9077 -3.4577 beta_pH(log_k2)  0.54151  0.2124  0.8706 log_tb           2.67815  2.3846  2.9717 beta_pH(log_tb) -0.09889 -0.4258  0.2280 a.1              4.49487  3.7766  5.2132 SD.log_k1        0.37191  0.2370  0.5068 SD.log_k2        0.29210  0.0994  0.4848 SD.log_tb        0.25353 -0.0664  0.5735  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_tb  log_k1           0.2301                                         beta_pH(log_k1) -0.0452 -0.2842                                 log_k2          -0.0029 -0.0322 -0.0008                         beta_pH(log_k2) -0.0071  0.0000 -0.0391 -0.2655                 log_tb          -0.1003 -0.2052  0.1180 -0.4197  0.1794         beta_pH(log_tb)  0.0097  0.1125 -0.1265  0.1894 -0.3653 -0.3449  Random effects:             est.   lower  upper SD.log_k1 0.3719  0.2370 0.5068 SD.log_k2 0.2921  0.0994 0.4848 SD.log_tb 0.2535 -0.0664 0.5735  Variance model:      est. lower upper a.1 4.495 3.777 5.213  Backtransformed parameters:            est.    lower    upper meso_0 93.32599 91.46575 95.18624 k1      0.04573  0.03752  0.05574 k2      0.02516  0.02009  0.03150 tb     14.55810 10.85502 19.52445  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 15.65 79.63    23.97   15.16   27.55 saemix version used for fitting:      3.4  mkin version used for pre-fitting:  1.2.11  R version used for fitting:         4.5.1  Date of fit:     Fri Sep 12 22:15:50 2025  Date of summary: Fri Nov 28 09:15:25 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.425 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   766.5 775.4 -373.2  Optimised parameters:                    est.    lower   upper meso_0          93.3251 91.49823 95.1520 log_k1          -3.0535 -3.24879 -2.8582 beta_pH(log_k1)  0.4567  0.22400  0.6894 log_k2          -3.7439 -3.94307 -3.5447 beta_pH(log_k2)  0.4982  0.20644  0.7899 log_tb           2.7040  2.33033  3.0777 a.1              4.4452  3.73537  5.1551 SD.log_k1        0.3570  0.22104  0.4930 SD.log_k2        0.2252  0.01864  0.4318 SD.log_tb        0.5488  0.24560  0.8521  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_k1           0.2233                                 beta_pH(log_k1) -0.0453 -0.2955                         log_k2           0.0028 -0.0420  0.0126                 beta_pH(log_k2) -0.0116  0.0112 -0.0667 -0.2097         log_tb          -0.0658 -0.1928  0.0913 -0.2843  0.1210  Random effects:             est.   lower  upper SD.log_k1 0.3570 0.22104 0.4930 SD.log_k2 0.2252 0.01864 0.4318 SD.log_tb 0.5488 0.24560 0.8521  Variance model:      est. lower upper a.1 4.445 3.735 5.155  Backtransformed parameters:            est.    lower    upper meso_0 93.32513 91.49823 95.15204 k1      0.04719  0.03882  0.05737 k2      0.02366  0.01939  0.02888 tb     14.93925 10.28132 21.70744  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 14.69 82.45    24.82   14.69   29.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Session info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"R version 4.5.2 (2025-10-31) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 13 (trixie)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.1  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.1;  LAPACK version 3.12.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] saemix_3.4  npde_3.5    knitr_1.50  mkin_1.2.10  loaded via a namespace (and not attached):  [1] gtable_0.3.6       jsonlite_2.0.0     dplyr_1.1.4        compiler_4.5.2      [5] tidyselect_1.2.1   dichromat_2.0-0.1  gridExtra_2.3      jquerylib_0.1.4     [9] systemfonts_1.2.3  scales_1.4.0       textshaping_1.0.1  yaml_2.3.10        [13] fastmap_1.2.0      lattice_0.22-7     ggplot2_3.5.2      R6_2.6.1           [17] generics_0.1.4     lmtest_0.9-40      MASS_7.3-65        htmlwidgets_1.6.4  [21] tibble_3.3.0       desc_1.4.3         bslib_0.9.0        pillar_1.10.2      [25] RColorBrewer_1.1-3 rlang_1.1.6        cachem_1.1.0       xfun_0.52          [29] fs_1.6.6           sass_0.4.10        cli_3.6.5          pkgdown_2.1.3      [33] magrittr_2.0.3     digest_0.6.37      grid_4.5.2         mclust_6.1.1       [37] lifecycle_1.0.4    nlme_3.1-168       vctrs_0.6.5        evaluate_1.0.3     [41] glue_1.8.0         farver_2.1.2       ragg_1.4.0         zoo_1.8-14         [45] colorspace_2.1-1   rmarkdown_2.29     tools_4.5.2        pkgconfig_2.0.3    [49] htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023.html","id":"hardware-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Hardware info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64933716 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"purpose document test demonstrate nonlinear hierarchical models (NLHM) based parent degradation models SFO, FOMC, DFOP HS can fitted mkin package, also considering influence covariates like soil pH different degradation parameters. case studies, SFORB parameterisation biexponential decline shown advantages DFOP parameterisation, SFORB included list tested models well. mkin package used version 1.2.10, contains functions used evaluations. saemix package used backend fitting NLHM, also loaded make convergence plot function available. document processed knitr package, also provides kable function used improve display tabular data R markdown documents. parallel processing, parallel package used.","code":"library(mkin) library(knitr) library(saemix) library(parallel) n_cores <- detectCores() if (Sys.info()[\"sysname\"] == \"Windows\") {   cl <- makePSOCKcluster(n_cores) } else {   cl <- makeForkCluster(n_cores) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"Introduction","what":"Test data","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following tables show covariate data 18 datasets read spreadsheet file. Covariate data Dataset Richmond Dataset Richmond 2 Dataset ERTC Dataset Toulouse Dataset Picket Piece Dataset 721 Dataset 722 Dataset 723 Dataset 724 Dataset 725 Dataset 727 Dataset 728 Dataset 729 Dataset 730 Dataset 731 Dataset 732 Dataset 741 Dataset 742","code":"data_file <- system.file(   \"testdata\", \"mesotrione_soil_efsa_2016.xlsx\", package = \"mkin\") meso_ds <- read_spreadsheet(data_file, parent_only = TRUE) pH <- attr(meso_ds, \"covariates\") kable(pH, caption = \"Covariate data\") for (ds_name in names(meso_ds)) {   print(     kable(mkin_long_to_wide(meso_ds[[ds_name]]),       caption = paste(\"Dataset\", ds_name),       booktabs = TRUE, row.names = FALSE)) }"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"","what":"Separate evaluations","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"order obtain suitable starting parameters NLHM fits, separate fits five models data soil generated using mmkin function mkin package. first step, constant variance assumed. Convergence checked status function. tables , OK indicates convergence C indicates failure converge. separate fits constant variance converged, exception two FOMC fits, one SFORB fit one HS fit. two-component error model, set fits converge larger, convergence problems appearing number non-SFO fits.","code":"deg_mods <- c(\"SFO\", \"FOMC\", \"DFOP\", \"SFORB\", \"HS\") f_sep_const <- mmkin(   deg_mods,   meso_ds,   error_model = \"const\",   cluster = cl,   quiet = TRUE) status(f_sep_const[, 1:5]) |> kable() status(f_sep_const[, 6:18]) |> kable() f_sep_tc <- update(f_sep_const, error_model = \"tc\") status(f_sep_tc[, 1:5]) |> kable() status(f_sep_tc[, 6:18]) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hierarchical-models-without-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models without covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following code fits hierarchical kinetic models ten combinations five different degradation models two different error models parallel. fits terminate without errors (status OK). model comparisons show fits constant variance consistently preferable corresponding fits two-component error data. confirmed fact parameter b.1 (relative standard deviation fits obtained saemix package), ill-defined fits. obtaining fits well-defined random effects, update set fits, excluding random effects ill-defined according illparms function. updated fits terminate without errors. ill-defined errors remain fits constant variance.","code":"f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) status(f_saem_1) |> kable() anova(f_saem_1) |> kable(digits = 1) illparms(f_saem_1) |> kable() f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() illparms(f_saem_2) |> kable()"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hierarchical-models-with-covariate","dir":"Articles > Web_only","previous_headings":"","what":"Hierarchical models with covariate","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"following sections, hierarchical fits including model influence pH selected degradation parameters shown parent models. Constant variance selected error model based fits without covariate effects. Random effects ill-defined fits without pH influence excluded. potential influence soil pH included parameters well-defined random effect, experience shown parameters significant pH effect found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"sfo","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFO","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"parameter showing pH influence table beta_pH(log_k_meso). confidence interval include zero, indicating influence soil pH log degradation rate constant significantly greater zero. comparison SFO fit without covariate effect confirms considering soil pH improves model, comparison AIC BIC likelihood ratio test.  Endpoints model covariates default calculated median covariate values. quantile can adapted, specific covariate value can given shown .","code":"sfo_pH <- saem(f_sep_const[\"SFO\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_k_meso ~ pH)) summary(sfo_pH)$confint_trans |> kable(digits = 2) anova(f_saem_2[[\"SFO\", \"const\"]], sfo_pH, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"SFO\", \"const\"]]    4 797.56 801.12 -394.78                          sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(sfo_pH) endpoints(sfo_pH) $covariates       pH 50% 5.75  $distimes          DT50     DT90 meso 18.52069 61.52441 endpoints(sfo_pH, covariate_quantile = 0.9) $covariates       pH 90% 7.13  $distimes          DT50     DT90 meso 8.237019 27.36278 endpoints(sfo_pH, covariates = c(pH = 7.0)) $covariates      pH User  7  $distimes         DT50    DT90 meso 8.89035 29.5331"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"fomc","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"FOMC","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"case SFO, confidence interval slope parameter (beta_pH(log_alpha)) quantifying influence soil pH include zero, model comparison clearly indicates model covariate influence preferable. However, random effect alpha well-defined inclusion covariate effect (confidence interval SD.log_alpha includes zero). Therefore, model updated without random effect, ill-defined parameters remain. Model comparison indicates including pH dependence significantly improves fit, reduced model covariate influence results preferable FOMC fit.","code":"fomc_pH <- saem(f_sep_const[\"FOMC\", ], no_random_effect = \"meso_0\", covariates = pH,   covariate_models = list(log_alpha ~ pH)) summary(fomc_pH)$confint_trans |> kable(digits = 2) illparms(fomc_pH) [1] \"sd(log_alpha)\" fomc_pH_2 <- update(fomc_pH, no_random_effect = c(\"meso_0\", \"log_alpha\")) illparms(fomc_pH_2) anova(f_saem_2[[\"FOMC\", \"const\"]], fomc_pH, fomc_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"FOMC\", \"const\"]]    5 783.25 787.71 -386.63                          fomc_pH_2                      6 767.49 772.83 -377.75 17.762  1  2.503e-05 *** fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(fomc_pH_2)$confint_trans |> kable(digits = 2) plot(fomc_pH_2) endpoints(fomc_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back meso 17.30248 82.91343 24.95943 endpoints(fomc_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back meso 6.986239 27.02927 8.136621"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"dfop","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"DFOP","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"DFOP fits without covariate effects, random effects two degradation parameters (k2 g) identifiable. fit pH dependent degradation parameters obtained excluding random effects refined DFOP fit without covariate influence, including covariate models two identifiable parameters k2 g. corresponding parameters influence soil pH beta_pH(log_k2) influence soil pH k2, beta_pH(g_qlogis) influence g. Confidence intervals neither include zero, indicating significant difference zero. However, random effect g now ill-defined. fit updated without ill-defined random effect. Now, slope parameter pH effect g ill-defined. Therefore, another attempt made without corresponding covariate model. random effect g ill-defined, fit repeated without . ill-defined parameters remain, model comparison suggests previous model dfop_pH_2 two pH dependent parameters preferable, based information criteria well based likelihood ratio test. focussing parameter identifiability using test confidence interval includes zero, dfop_pH_4 still preferred model. However, kept mind parameter confidence intervals constructed using simple linearisation likelihood. confidence interval random effect g marginally includes zero, suggested acceptable, dfop_pH_2 can considered preferable model.","code":"summary(f_saem_2[[\"DFOP\", \"const\"]])$confint_trans |> kable(digits = 2) dfop_pH <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH)) summary(dfop_pH)$confint_trans |> kable(digits = 2) illparms(dfop_pH) [1] \"sd(g_qlogis)\" dfop_pH_2 <- update(dfop_pH,   no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_2) [1] \"beta_pH(g_qlogis)\" dfop_pH_3 <- saem(f_sep_const[\"DFOP\", ], no_random_effect = c(\"meso_0\", \"log_k1\"),   covariates = pH,   covariate_models = list(log_k2 ~ pH)) illparms(dfop_pH_3) [1] \"sd(g_qlogis)\" dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c(\"meso_0\", \"log_k1\", \"g_qlogis\")) illparms(dfop_pH_4) anova(f_saem_2[[\"DFOP\", \"const\"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4) Data: 116 observations of 1 variable(s) grouped in 18 datasets                              npar    AIC    BIC     Lik f_saem_2[[\"DFOP\", \"const\"]]    7 782.94 789.18 -384.47 dfop_pH_4                      7 767.35 773.58 -376.68 dfop_pH_2                      8 765.14 772.26 -374.57 dfop_pH_3                      8 769.00 776.12 -376.50 dfop_pH                        9 769.10 777.11 -375.55 anova(dfop_pH_2, dfop_pH_4, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)   dfop_pH_4    7 767.35 773.58 -376.68                        dfop_pH_2    8 765.14 772.26 -374.57 4.2153  1    0.04006 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(dfop_pH_2) endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"sforb","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"SFORB","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"confidence interval beta_pH(log_k_meso_bound_free) includes zero, indicating influence soil pH k_meso_bound_free reliably quantified. Also, confidence interval random effect parameter (SD.log_k_meso_bound_free) includes zero. Using illparms function, ill-defined parameters can found conveniently. remove ill-defined parameters, second variant SFORB model pH influence fitted. ill-defined parameters remain. model comparison SFORB fits includes refined model without covariate effect, versions SFORB fit covariate effect. first model including pH influence preferable based information criteria likelihood ratio test. However, fully identifiable, second model selected.","code":"sforb_pH <- saem(f_sep_const[\"SFORB\", ], no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\"),   covariates = pH,   covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH)) summary(sforb_pH)$confint_trans |> kable(digits = 2) illparms(sforb_pH) [1] \"sd(log_k_meso_bound_free)\"      \"beta_pH(log_k_meso_bound_free)\" sforb_pH_2 <- update(sforb_pH,   no_random_effect = c(\"meso_free_0\", \"log_k_meso_free_bound\", \"log_k_meso_bound_free\"),   covariate_models = list(log_k_meso_free ~ pH)) illparms(sforb_pH_2) anova(f_saem_2[[\"SFORB\", \"const\"]], sforb_pH, sforb_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                               npar    AIC    BIC     Lik   Chisq Df Pr(>Chisq)   f_saem_2[[\"SFORB\", \"const\"]]    7 783.40 789.63 -384.70                         sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0              sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 * --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(sforb_pH_2)$confint_trans |> kable(digits = 2) plot(sforb_pH_2) endpoints(sforb_pH_2) $covariates       pH 50% 5.75  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.09735824 0.02631699 0.31602120   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.86549 73.15824 22.02282     7.119554     26.33839 endpoints(sforb_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $ff meso_free          1   $SFORB    meso_b1    meso_b2     meso_g  0.13315233 0.03795988 0.61186191   $distimes          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 7.932495 36.93311 11.11797     5.205671        18.26"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hs","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"HS","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"According output illparms function, random effect break time tb reliably quantified, neither can influence soil pH tb. fit repeated without corresponding covariate model, ill-defined parameters remain. Model comparison confirms model preferable fit without covariate influence, also first version covariate influence.","code":"hs_pH <- saem(f_sep_const[\"HS\", ], no_random_effect = c(\"meso_0\"),   covariates = pH,   covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH)) summary(hs_pH)$confint_trans |> kable(digits = 2) illparms(hs_pH) [1] \"sd(log_tb)\"      \"beta_pH(log_tb)\" hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) illparms(hs_pH_2) anova(f_saem_2[[\"HS\", \"const\"]], hs_pH, hs_pH_2, test = TRUE) Data: 116 observations of 1 variable(s) grouped in 18 datasets                            npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     f_saem_2[[\"HS\", \"const\"]]    8 780.08 787.20 -382.04                          hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 *** hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000     --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 summary(hs_pH_2)$confint_trans |> kable(digits = 2) plot(hs_pH_2) endpoints(hs_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 14.68725 82.45287 24.82079 14.68725 29.29299 endpoints(hs_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.298536 38.85371 11.69613 8.298536 15.71561"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"comparison-across-parent-models","dir":"Articles > Web_only","previous_headings":"Hierarchical models with covariate","what":"Comparison across parent models","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"model reduction models pH influence, compared . DFOP model pH influence k2 g random effect k2 finally selected best fit. endpoints resulting model listed . Please refer Appendix detailed listing.","code":"anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2) Data: 116 observations of 1 variable(s) grouped in 18 datasets             npar    AIC    BIC     Lik sfo_pH        5 783.09 787.54 -386.54 fomc_pH_2     6 767.49 772.83 -377.75 dfop_pH_4     7 767.35 773.58 -376.68 sforb_pH_2    7 770.94 777.17 -378.47 dfop_pH_2     8 765.14 772.26 -374.57 hs_pH_2      10 766.47 775.37 -373.23 endpoints(dfop_pH_2) $covariates       pH 50% 5.75  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 18.36876 73.51841 22.13125 4.191901 23.98672 endpoints(dfop_pH_2, covariates = c(pH = 7)) $covariates      pH User  7  $distimes          DT50     DT90 DT50back  DT50_k1  DT50_k2 meso 8.346428 28.34437 8.532507 4.191901 8.753618"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"evaluations demonstrate covariate effects can included types parent degradation models. models can refined make fully identifiable.","code":""},{"path":[]},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"fits-without-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits without covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchical SFO fit constant variance  Hierarchical FOMC fit constant variance  Hierarchical DFOP fit constant variance  Hierarchical SFORB fit constant variance  Hierarchical HS fit constant variance","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.682 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling   AIC   BIC logLik   800 804.5   -395  Optimised parameters:                  est.    lower   upper meso_0        92.0705  89.9917 94.1493 log_k_meso    -3.1641  -3.4286 -2.8996 a.1            5.4628   4.6421  6.2835 SD.meso_0      0.0611 -98.3545 98.4767 SD.log_k_meso  0.5616   0.3734  0.7499  Correlation:             meso_0 log_k_meso 0.1132  Random effects:                 est.    lower   upper SD.meso_0     0.0611 -98.3545 98.4767 SD.log_k_meso 0.5616   0.3734  0.7499  Variance model:      est. lower upper a.1 5.463 4.642 6.284  Backtransformed parameters:            est.    lower    upper meso_0 92.07053 89.99172 94.14933 k_meso  0.04225  0.03243  0.05505  Estimated disappearance times:       DT50 DT90 meso 16.41 54.5 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 0.817 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 793.6 -386.7  Optimised parameters:                 est.     lower   upper meso_0       93.5648  91.42864 95.7009 log_alpha     0.7645   0.28068  1.2484 log_beta      3.6597   3.05999  4.2594 a.1           5.0708   4.29823  5.8435 SD.meso_0     0.1691 -34.01517 34.3535 SD.log_alpha  0.3764   0.05834  0.6945 SD.log_beta   0.3903  -0.06074  0.8414  Correlation:            meso_0  log_lph log_alpha -0.2839         log_beta  -0.3443  0.8855  Random effects:                est.     lower   upper SD.meso_0    0.1691 -34.01517 34.3535 SD.log_alpha 0.3764   0.05834  0.6945 SD.log_beta  0.3903  -0.06074  0.8414  Variance model:      est. lower upper a.1 5.071 4.298 5.843  Backtransformed parameters:          est.  lower  upper meso_0 93.565 91.429 95.701 alpha   2.148  1.324  3.485 beta   38.850 21.327 70.770  Estimated disappearance times:      DT50  DT90 DT50back meso 14.8 74.64    22.47 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.188 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.6 795.6 -384.8  Optimised parameters:                est.     lower   upper meso_0      93.6684  91.63599 95.7008 log_k1      -1.7354  -2.61433 -0.8565 log_k2      -3.4015  -3.73323 -3.0697 g_qlogis    -1.6341  -2.66133 -0.6069 a.1          4.7803   4.01269  5.5479 SD.meso_0    0.1661 -30.97086 31.3031 SD.log_k1    0.1127  -2.59680  2.8223 SD.log_k2    0.6394   0.41499  0.8638 SD.g_qlogis  0.8166   0.09785  1.5353  Correlation:           meso_0  log_k1  log_k2  log_k1    0.1757                 log_k2    0.0199  0.2990         g_qlogis  0.0813 -0.7431 -0.3826  Random effects:               est.     lower   upper SD.meso_0   0.1661 -30.97086 31.3031 SD.log_k1   0.1127  -2.59680  2.8223 SD.log_k2   0.6394   0.41499  0.8638 SD.g_qlogis 0.8166   0.09785  1.5353  Variance model:     est. lower upper a.1 4.78 4.013 5.548  Backtransformed parameters:            est.    lower    upper meso_0 93.66841 91.63599 95.70082 k1      0.17633  0.07322  0.42466 k2      0.03332  0.02392  0.04643 g       0.16327  0.06529  0.35277  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 16.04 63.75    19.19   3.931    20.8 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:35 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.223 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   787.4 795.4 -384.7  Optimised parameters:                             est.    lower  upper meso_free_0              93.6285  91.6262 95.631 log_k_meso_free          -2.8314  -3.1375 -2.525 log_k_meso_free_bound    -3.2213  -4.4695 -1.973 log_k_meso_bound_free    -2.4246  -3.5668 -1.282 a.1                       4.7372   3.9542  5.520 SD.meso_free_0            0.1634 -32.7769 33.104 SD.log_k_meso_free        0.4885   0.3080  0.669 SD.log_k_meso_free_bound  0.2876  -1.7955  2.371 SD.log_k_meso_bound_free  0.9942   0.2181  1.770  Correlation:                        ms_fr_0 lg_k_m_ lg_k_ms_f_ log_k_meso_free        0.2332                    log_k_meso_free_bound  0.1100  0.5964            log_k_meso_bound_free -0.0413  0.3697  0.8025     Random effects:                            est.    lower  upper SD.meso_free_0           0.1634 -32.7769 33.104 SD.log_k_meso_free       0.4885   0.3080  0.669 SD.log_k_meso_free_bound 0.2876  -1.7955  2.371 SD.log_k_meso_bound_free 0.9942   0.2181  1.770  Variance model:      est. lower upper a.1 4.737 3.954  5.52  Backtransformed parameters:                       est.    lower    upper meso_free_0       93.62849 91.62622 95.63075 k_meso_free        0.05893  0.04339  0.08004 k_meso_free_bound  0.03990  0.01145  0.13903 k_meso_bound_free  0.08851  0.02825  0.27736  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.15333 0.03402 0.20881   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 14.79 60.81     18.3        4.521        20.37 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:36 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.307 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   781.9 789.9   -382  Optimised parameters:               est.    lower   upper meso_0    93.34242  91.4730 95.2118 log_k1    -2.77312  -3.0826 -2.4637 log_k2    -3.61854  -3.8430 -3.3941 log_tb     2.00266   1.3357  2.6696 a.1        4.47693   3.7059  5.2479 SD.meso_0  0.07963 -63.1661 63.3253 SD.log_k1  0.47817   0.2467  0.7097 SD.log_k2  0.39216   0.2137  0.5706 SD.log_tb  0.94683   0.4208  1.4728  Correlation:         meso_0  log_k1  log_k2  log_k1  0.1627                 log_k2  0.0063 -0.0301         log_tb  0.0083 -0.3931 -0.1225  Random effects:              est.    lower   upper SD.meso_0 0.07963 -63.1661 63.3253 SD.log_k1 0.47817   0.2467  0.7097 SD.log_k2 0.39216   0.2137  0.5706 SD.log_tb 0.94683   0.4208  1.4728  Variance model:      est. lower upper a.1 4.477 3.706 5.248  Backtransformed parameters:            est.    lower    upper meso_0 93.34242 91.47303 95.21181 k1      0.06247  0.04584  0.08512 k2      0.02682  0.02143  0.03357 tb      7.40872  3.80282 14.43376  Estimated disappearance times:      DT50 DT90 DT50back DT50_k1 DT50_k2 meso   16   76    22.88    11.1   25.84"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"fits-with-covariate-effects","dir":"Articles > Web_only","previous_headings":"Appendix > Hierarchical fit listings","what":"Fits with covariate effects","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"Hierarchichal SFO fit pH influence  Hierarchichal FOMC fit pH influence  Refined hierarchichal FOMC fit pH influence  Hierarchichal DFOP fit pH influence  Refined hierarchical DFOP fit pH influence  refined hierarchical DFOP fit pH influence  Hierarchichal SFORB fit pH influence  Refined hierarchichal SFORB fit pH influence  Hierarchichal HS fit pH influence  Refined hierarchichal HS fit pH influence","code":"saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:49 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - k_meso * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.739 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:     meso_0 log_k_meso      90.832     -3.192   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):            meso_0 log_k_meso meso_0      6.752     0.0000 log_k_meso  0.000     0.9155  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   783.1 787.5 -386.5  Optimised parameters:                        est.   lower   upper meso_0              91.3481 89.2688 93.4275 log_k_meso          -6.6614 -7.9715 -5.3514 beta_pH(log_k_meso)  0.5871  0.3684  0.8059 a.1                  5.4750  4.7085  6.2415 SD.log_k_meso        0.3471  0.2258  0.4684  Correlation:                      meso_0  lg_k_ms log_k_meso           0.0414         beta_pH(log_k_meso) -0.0183 -0.9917  Random effects:                 est.  lower  upper SD.log_k_meso 0.3471 0.2258 0.4684  Variance model:      est. lower upper a.1 5.475 4.709 6.242  Backtransformed parameters:             est.     lower     upper meso_0 91.348139 8.927e+01 93.427476 k_meso  0.001279 3.452e-04  0.004741  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 meso 18.52 61.52 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:51 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.076 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.1 776.3   -378  Optimised parameters:                         est.      lower   upper meso_0             92.840646  90.750461 94.9308 log_alpha          -2.206602  -3.494546 -0.9187 beta_pH(log_alpha)  0.577505   0.369805  0.7852 log_beta            4.214099   3.438851  4.9893 a.1                 5.027768   4.322028  5.7335 SD.log_alpha        0.004034 -23.766993 23.7751 SD.log_beta         0.374640   0.009252  0.7400  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.0865                 beta_pH(log_alpha) -0.0789 -0.8704         log_beta           -0.3544  0.3302  0.1628  Random effects:                  est.      lower upper SD.log_alpha 0.004034 -23.766993 23.78 SD.log_beta  0.374640   0.009252  0.74  Variance model:      est. lower upper a.1 5.028 4.322 5.734  Backtransformed parameters:           est.    lower    upper meso_0 92.8406 90.75046  94.9308 alpha   0.1101  0.03036   0.3991 beta   67.6332 31.15113 146.8404  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back meso 17.28 76.37    22.99 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:55 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.361 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:    meso_0 log_alpha  log_beta    93.0520    0.6008    3.4176   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):           meso_0 log_alpha log_beta meso_0     6.287      0.00    0.000 log_alpha  0.000      1.53    0.000 log_beta   0.000      0.00    1.724  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   767.5 772.8 -377.7  Optimised parameters:                       est.   lower   upper meso_0             93.0536 90.9771 95.1300 log_alpha          -2.9054 -4.1803 -1.6304 beta_pH(log_alpha)  0.6590  0.4437  0.8744 log_beta            3.9549  3.2860  4.6239 a.1                 4.9784  4.2815  5.6754 SD.log_beta         0.4019  0.2632  0.5406  Correlation:                     meso_0  log_lph bt_H(_) log_alpha          -0.0397                 beta_pH(log_alpha) -0.0899 -0.9146         log_beta           -0.3473  0.2038  0.1919  Random effects:               est.  lower  upper SD.log_beta 0.4019 0.2632 0.5406  Variance model:      est. lower upper a.1 4.978 4.281 5.675  Backtransformed parameters:            est.    lower    upper meso_0 93.05359 90.97713  95.1300 alpha   0.05473  0.01529   0.1958 beta   52.19251 26.73597 101.8874  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:      DT50  DT90 DT50back meso 17.3 82.91    24.96 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 19:59:58 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.758 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.1 777.1 -375.5  Optimised parameters:                         est.    lower    upper meso_0             92.843344  90.8464 94.84028 log_k1             -2.815685  -3.0888 -2.54261 log_k2            -11.479779 -15.3203 -7.63923 beta_pH(log_k2)     1.308417   0.6948  1.92203 g_qlogis            3.133036   0.4657  5.80035 beta_pH(g_qlogis)  -0.565988  -1.0394 -0.09262 a.1                 4.955518   4.2597  5.65135 SD.log_k2           0.758963   0.4685  1.04943 SD.g_qlogis         0.005215  -9.9561  9.96656  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.2706                                 log_k2            -0.0571  0.1096                         beta_pH(log_k2)    0.0554 -0.1291 -0.9937                 g_qlogis          -0.1125 -0.5062 -0.1305  0.1294         beta_pH(g_qlogis)  0.1267  0.4226  0.0419 -0.0438 -0.9864  Random effects:                 est.   lower upper SD.log_k2   0.758963  0.4685 1.049 SD.g_qlogis 0.005215 -9.9561 9.967  Variance model:      est. lower upper a.1 4.956  4.26 5.651  Backtransformed parameters:             est.     lower     upper meso_0 9.284e+01 9.085e+01 9.484e+01 k1     5.986e-02 4.556e-02 7.866e-02 k2     1.034e-05 2.221e-07 4.812e-04 g      9.582e-01 6.144e-01 9.970e-01  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 20.23 88.45    26.62   11.58   36.23 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:03 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 4.465 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   765.1 772.3 -374.6  Optimised parameters:                      est.    lower    upper meso_0            93.3333  91.2427 95.42394 log_k1            -1.7997  -2.9124 -0.68698 log_k2            -8.1810 -10.1819 -6.18008 beta_pH(log_k2)    0.8064   0.4903  1.12257 g_qlogis           3.3513  -1.1792  7.88182 beta_pH(g_qlogis) -0.8672  -1.7661  0.03177 a.1                4.9158   4.2277  5.60390 SD.log_k2          0.3946   0.2565  0.53281  Correlation:                    meso_0  log_k1  log_k2  b_H(_2) g_qlogs log_k1             0.1730                                 log_k2             0.0442  0.5370                         beta_pH(log_k2)   -0.0392 -0.4880 -0.9923                 g_qlogis          -0.1536  0.1431 -0.1129  0.1432         beta_pH(g_qlogis)  0.1504 -0.3151 -0.0196 -0.0212 -0.9798  Random effects:             est.  lower  upper SD.log_k2 0.3946 0.2565 0.5328  Variance model:      est. lower upper a.1 4.916 4.228 5.604  Backtransformed parameters:             est.     lower    upper meso_0 9.333e+01 9.124e+01 95.42394 k1     1.654e-01 5.435e-02  0.50309 k2     2.799e-04 3.785e-05  0.00207 g      9.661e-01 2.352e-01  0.99962  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 18.37 73.52    22.13   4.192   23.99 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:10 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.781 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:   meso_0   log_k1   log_k2 g_qlogis  93.14689 -2.05241 -3.53079 -0.09522   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):          meso_0 log_k1 log_k2 g_qlogis meso_0    6.418  0.000  0.000     0.00 log_k1    0.000  1.018  0.000     0.00 log_k2    0.000  0.000  1.694     0.00 g_qlogis  0.000  0.000  0.000     2.37  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   767.4 773.6 -376.7  Optimised parameters:                    est.    lower   upper meso_0          93.3011  91.1905 95.4118 log_k1          -2.1487  -2.7607 -1.5367 log_k2          -8.1039 -10.4225 -5.7853 beta_pH(log_k2)  0.7821   0.4126  1.1517 g_qlogis        -1.0373  -1.9337 -0.1409 a.1              5.0095   4.3082  5.7108 SD.log_k2        0.4622   0.3009  0.6235  Correlation:                  meso_0  log_k1  log_k2  b_H(_2) log_k1           0.2179                         log_k2           0.0337  0.5791                 beta_pH(log_k2) -0.0326 -0.5546 -0.9932         g_qlogis         0.0237 -0.8479 -0.6571  0.6123  Random effects:             est.  lower  upper SD.log_k2 0.4622 0.3009 0.6235  Variance model:      est. lower upper a.1 5.009 4.308 5.711  Backtransformed parameters:             est.     lower     upper meso_0 9.330e+01 9.119e+01 95.411751 k1     1.166e-01 6.325e-02  0.215084 k2     3.024e-04 2.975e-05  0.003072 g      2.617e-01 1.263e-01  0.464832  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 17.09 73.67    22.18   5.943   25.54 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:14 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 3.54 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   768.8 776.8 -375.4  Optimised parameters:                                   est.    lower   upper meso_free_0                    93.4204  91.3213 95.5195 log_k_meso_free                -5.3742  -6.9366 -3.8117 beta_pH(log_k_meso_free)        0.4232   0.1769  0.6695 log_k_meso_free_bound          -3.4889  -4.9243 -2.0535 log_k_meso_bound_free          -9.9797 -19.2232 -0.7362 beta_pH(log_k_meso_bound_free)  1.2290  -0.2107  2.6687 a.1                             4.9031   4.1795  5.6268 SD.log_k_meso_free              0.3454   0.2252  0.4656 SD.log_k_meso_bound_free        0.1277  -1.9459  2.2012  Correlation:                                 ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_ log_k_meso_free                 0.1493                                        beta_pH(log_k_meso_free)       -0.0930 -0.9854                                log_k_meso_free_bound           0.2439  0.4621 -0.3492                        log_k_meso_bound_free           0.2188  0.1292 -0.0339   0.7287               beta_pH(log_k_meso_bound_free) -0.2216 -0.0797 -0.0111  -0.6566    -0.9934     Random effects:                            est.   lower  upper SD.log_k_meso_free       0.3454  0.2252 0.4656 SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012  Variance model:      est. lower upper a.1 4.903  4.18 5.627  Backtransformed parameters:                        est.     lower    upper meso_free_0       9.342e+01 9.132e+01 95.51946 k_meso_free       4.635e-03 9.716e-04  0.02211 k_meso_free_bound 3.054e-02 7.268e-03  0.12829 k_meso_bound_free 4.633e-05 4.482e-09  0.47894  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g   0.1121  0.0256  0.3148   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.42 75.2    22.64        6.185        27.08 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:18 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *            meso_free + k_meso_bound_free * meso_bound d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *            meso_bound  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 2.815 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters:           meso_free_0       log_k_meso_free log_k_meso_free_bound                 93.147                -2.305                -4.230  log_k_meso_bound_free                 -3.761   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):                       meso_free_0 log_k_meso_free log_k_meso_free_bound meso_free_0                 6.418          0.0000                 0.000 log_k_meso_free             0.000          0.9276                 0.000 log_k_meso_free_bound       0.000          0.0000                 2.272 log_k_meso_bound_free       0.000          0.0000                 0.000                       log_k_meso_bound_free meso_free_0                           0.000 log_k_meso_free                       0.000 log_k_meso_free_bound                 0.000 log_k_meso_bound_free                 1.447  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   770.9 777.2 -378.5  Optimised parameters:                             est.   lower   upper meso_free_0              93.3196 91.1633 95.4760 log_k_meso_free          -6.1460 -7.4306 -4.8614 beta_pH(log_k_meso_free)  0.5435  0.3329  0.7542 log_k_meso_free_bound    -3.8001 -5.2027 -2.3975 log_k_meso_bound_free    -2.9462 -4.2565 -1.6359 a.1                       5.0825  4.3793  5.7856 SD.log_k_meso_free        0.3338  0.2175  0.4502  Correlation:                           ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_ log_k_meso_free           0.1086                            beta_pH(log_k_meso_free) -0.0426 -0.9821                    log_k_meso_free_bound     0.2513  0.1717 -0.0409            log_k_meso_bound_free     0.1297  0.1171 -0.0139  0.9224     Random effects:                      est.  lower  upper SD.log_k_meso_free 0.3338 0.2175 0.4502  Variance model:      est. lower upper a.1 5.082 4.379 5.786  Backtransformed parameters:                        est.     lower    upper meso_free_0       93.319649 9.116e+01 95.47601 k_meso_free        0.002142 5.928e-04  0.00774 k_meso_free_bound  0.022369 5.502e-03  0.09095 k_meso_bound_free  0.052539 1.417e-02  0.19478  Covariates used for endpoints below:       pH 50% 5.75  Estimated Eigenvalues of SFORB model(s): meso_b1 meso_b2  meso_g  0.09736 0.02632 0.31602   Resulting formation fractions:           ff meso_free  1  Estimated disappearance times:       DT50  DT90 DT50back DT50_meso_b1 DT50_meso_b2 meso 16.87 73.16    22.02         7.12        26.34 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:20 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.849 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   769.8 779.6 -373.9  Optimised parameters:                     est.   lower   upper meso_0          93.32599 91.4658 95.1862 log_k1          -5.81463 -7.2710 -4.3583 beta_pH(log_k1)  0.47472  0.2334  0.7160 log_k2          -6.79633 -8.7605 -4.8322 beta_pH(log_k2)  0.54151  0.2124  0.8706 log_tb           3.24674  1.2470  5.2465 beta_pH(log_tb) -0.09889 -0.4258  0.2280 a.1              4.49487  3.7766  5.2132 SD.log_k1        0.37191  0.2370  0.5068 SD.log_k2        0.29210  0.0994  0.4848 SD.log_tb        0.25353 -0.0664  0.5735  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_tb  log_k1           0.0744                                         beta_pH(log_k1) -0.0452 -0.9915                                 log_k2           0.0066 -0.0363  0.0376                         beta_pH(log_k2) -0.0071  0.0372 -0.0391 -0.9939                 log_tb          -0.0238 -0.1483  0.1362 -0.3836  0.3696         beta_pH(log_tb)  0.0097  0.1359 -0.1265  0.3736 -0.3653 -0.9905  Random effects:             est.   lower  upper SD.log_k1 0.3719  0.2370 0.5068 SD.log_k2 0.2921  0.0994 0.4848 SD.log_tb 0.2535 -0.0664 0.5735  Variance model:      est. lower upper a.1 4.495 3.777 5.213  Backtransformed parameters:             est.     lower     upper meso_0 93.325994 9.147e+01 9.519e+01 k1      0.002984 6.954e-04 1.280e-02 k2      0.001118 1.568e-04 7.969e-03 tb     25.706437 3.480e+00 1.899e+02  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 15.65 79.63    23.97   15.16   27.55 saemix version used for fitting:      3.3  mkin version used for pre-fitting:  1.2.10  R version used for fitting:         4.5.0  Date of fit:     Tue May 13 20:00:22 2025  Date of summary: Tue May 13 20:00:23 2025   Equations: d_meso/dt = - ifelse(time <= tb, k1, k2) * meso  Data: 116 observations of 1 variable(s) grouped in 18 datasets  Model predictions using solution type analytical   Fitted in 1.439 s Using 300, 100 iterations and 3 chains  Variance model: Constant variance   Starting values for degradation parameters: meso_0 log_k1 log_k2 log_tb  92.920 -2.409 -3.295  2.471   Fixed degradation parameter values: None  Starting values for random effects (square root of initial entries in omega):        meso_0 log_k1 log_k2 log_tb meso_0  6.477 0.0000 0.0000   0.00 log_k1  0.000 0.8675 0.0000   0.00 log_k2  0.000 0.0000 0.4035   0.00 log_tb  0.000 0.0000 0.0000   1.16  Starting values for error model parameters: a.1    1   Results:  Likelihood computed by importance sampling     AIC   BIC logLik   766.5 775.4 -373.2  Optimised parameters:                    est.    lower   upper meso_0          93.3251 91.49823 95.1520 log_k1          -5.6796 -7.08789 -4.2714 beta_pH(log_k1)  0.4567  0.22400  0.6894 log_k2          -6.6083 -8.33839 -4.8781 beta_pH(log_k2)  0.4982  0.20644  0.7899 log_tb           2.7040  2.33033  3.0777 a.1              4.4452  3.73537  5.1551 SD.log_k1        0.3570  0.22104  0.4930 SD.log_k2        0.2252  0.01864  0.4318 SD.log_tb        0.5488  0.24560  0.8521  Correlation:                  meso_0  log_k1  b_H(_1) log_k2  b_H(_2) log_k1           0.0740                                 beta_pH(log_k1) -0.0453 -0.9912                         log_k2           0.0115 -0.0650  0.0661                 beta_pH(log_k2) -0.0116  0.0649 -0.0667 -0.9936         log_tb          -0.0658 -0.1135  0.0913 -0.1500  0.1210  Random effects:             est.   lower  upper SD.log_k1 0.3570 0.22104 0.4930 SD.log_k2 0.2252 0.01864 0.4318 SD.log_tb 0.5488 0.24560 0.8521  Variance model:      est. lower upper a.1 4.445 3.735 5.155  Backtransformed parameters:             est.     lower     upper meso_0 93.325134 9.150e+01 95.152036 k1      0.003415 8.352e-04  0.013962 k2      0.001349 2.392e-04  0.007611 tb     14.939247 1.028e+01 21.707445  Covariates used for endpoints below:       pH 50% 5.75  Estimated disappearance times:       DT50  DT90 DT50back DT50_k1 DT50_k2 meso 14.69 82.45    24.82   14.69   29.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Session info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] parallel  stats     graphics  grDevices utils     datasets  methods   [8] base       other attached packages: [1] rmarkdown_2.29  nvimcom_0.9-167 saemix_3.3      npde_3.5        [5] knitr_1.49      mkin_1.2.10      loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.9.0    dplyr_1.1.4       compiler_4.5.0     [5] tidyselect_1.2.1  gridExtra_2.3     jquerylib_0.1.4   systemfonts_1.2.1  [9] scales_1.3.0      textshaping_1.0.0 readxl_1.4.4      yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.6.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-65       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.9.0       [25] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51         [29] fs_1.6.5          sass_0.4.9        cli_3.6.4         pkgdown_2.1.1     [33] magrittr_2.0.3    digest_0.6.37     grid_4.5.0        mclust_6.1.1      [37] lifecycle_1.0.4   nlme_3.1-168      vctrs_0.6.5       evaluate_1.0.3    [41] glue_1.8.0        cellranger_1.1.0  codetools_0.2-20  ragg_1.3.3        [45] zoo_1.8-13        colorspace_2.1-1  tools_4.5.0       pkgconfig_2.0.3   [49] htmltools_0.5.8.1"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/mesotrione_parent_2023_prebuilt.html","id":"hardware-info","dir":"Articles > Web_only","previous_headings":"Appendix","what":"Hardware info","title":"Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione","text":"","code":"CPU model: AMD Ryzen 9 7950X 16-Core Processor MemTotal:       64927780 kB"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"","what":"Test data","title":"Benchmark timings for saem.mmkin","text":"Please refer vignette dimethenamid_2018 explanation following preprocessing.","code":"dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"parent-only","dir":"Articles > Web_only","previous_headings":"Test cases","what":"Parent only","title":"Benchmark timings for saem.mmkin","text":"model comparison suggests use SFORB model two-component error. comparison, keep DFOP model two-component error, competes SFORB biphasic curves. two models, random effects transformed parameters k2 k_DMTA_bound_free quantified.","code":"parent_mods <- c(\"SFO\", \"DFOP\", \"SFORB\", \"HS\") parent_sep_const <- mmkin(parent_mods, dmta_ds, quiet = TRUE, cores = n_cores) parent_sep_tc <- update(parent_sep_const, error_model = \"tc\")  t1 <- system.time(sfo_const <- saem(parent_sep_const[\"SFO\", ]))[[\"elapsed\"]] t2 <- system.time(dfop_const <- saem(parent_sep_const[\"DFOP\", ]))[[\"elapsed\"]] t3 <- system.time(sforb_const <- saem(parent_sep_const[\"SFORB\", ]))[[\"elapsed\"]] t4 <- system.time(hs_const <- saem(parent_sep_const[\"HS\", ]))[[\"elapsed\"]] t5 <- system.time(sfo_tc <- saem(parent_sep_tc[\"SFO\", ]))[[\"elapsed\"]] t6 <- system.time(dfop_tc <- saem(parent_sep_tc[\"DFOP\", ]))[[\"elapsed\"]] t7 <- system.time(sforb_tc <- saem(parent_sep_tc[\"SFORB\", ]))[[\"elapsed\"]] t8 <- system.time(hs_tc <- saem(parent_sep_tc[\"HS\", ]))[[\"elapsed\"]] anova(   sfo_const, dfop_const, sforb_const, hs_const,   sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1) illparms(dfop_tc) ## [1] \"sd(log_k2)\" illparms(sforb_tc) ## [1] \"sd(log_k_DMTA_bound_free)\""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"one-metabolite","dir":"Articles > Web_only","previous_headings":"Test cases","what":"One metabolite","title":"Benchmark timings for saem.mmkin","text":"remove parameters found ill-defined parent fits.","code":"one_met_mods <- list(   DFOP_SFO = mkinmod(     DMTA = mkinsub(\"DFOP\", \"M23\"),     M23 = mkinsub(\"SFO\")),   SFORB_SFO = mkinmod(     DMTA = mkinsub(\"SFORB\", \"M23\"),     M23 = mkinsub(\"SFO\")))  one_met_sep_const <- mmkin(one_met_mods, dmta_ds, error_model = \"const\",   cores = n_cores, quiet = TRUE) one_met_sep_tc <- mmkin(one_met_mods, dmta_ds, error_model = \"tc\",   cores = n_cores, quiet = TRUE)  t9 <- system.time(dfop_sfo_tc <- saem(one_met_sep_tc[\"DFOP_SFO\", ],     no_random_effect = \"log_k2\"))[[\"elapsed\"]] t10 <- system.time(sforb_sfo_tc <- saem(one_met_sep_tc[\"SFORB_SFO\", ],     no_random_effect = \"log_k_DMTA_bound_free\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"three-metabolites","dir":"Articles > Web_only","previous_headings":"Test cases","what":"Three metabolites","title":"Benchmark timings for saem.mmkin","text":"case three metabolites, keep SFORB model order limit time compiling vignette, fitting parallel may disturb benchmark. , include random effects ill-defined previous fits subsets degradation model.","code":"illparms(sforb_sfo_tc) three_met_mods <- list(   SFORB_SFO3_plus = mkinmod(     DMTA = mkinsub(\"SFORB\", c(\"M23\", \"M27\", \"M31\")),     M23 = mkinsub(\"SFO\"),     M27 = mkinsub(\"SFO\"),     M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE)))  three_met_sep_tc <- mmkin(three_met_mods, dmta_ds, error_model = \"tc\",   cores = n_cores, quiet = TRUE)  t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc[\"SFORB_SFO3_plus\", ],     no_random_effect = \"log_k_DMTA_bound_free\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"results","dir":"Articles > Web_only","previous_headings":"","what":"Results","title":"Benchmark timings for saem.mmkin","text":"Benchmarks available error models shown. intended improving mkin, comparing CPUs operating systems. trademarks belong respective owners.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"parent-only-1","dir":"Articles > Web_only","previous_headings":"Results","what":"Parent only","title":"Benchmark timings for saem.mmkin","text":"Constant variance SFO, DFOP, SFORB HS. Two-component error fits SFO, DFOP, SFORB HS.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"one-metabolite-1","dir":"Articles > Web_only","previous_headings":"Results","what":"One metabolite","title":"Benchmark timings for saem.mmkin","text":"Two-component error DFOP-SFO SFORB-SFO.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"three-metabolites-1","dir":"Articles > Web_only","previous_headings":"Results","what":"Three metabolites","title":"Benchmark timings for saem.mmkin","text":"Two-component error SFORB-SFO3-plus","code":""},{"path":"https://pkgdown.jrwb.de/mkin/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Johannes Ranke. Author, maintainer, copyright holder. Katrin Lindenberger. Contributor.           contributed mkinresplot() René Lehmann. Contributor.           ilr() invilr() Eurofins Regulatory AG. Copyright holder.           copyright contributions JR 2012-2014","code":""},{"path":"https://pkgdown.jrwb.de/mkin/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ranke J (2025). mkin: Kinetic Evaluation Chemical Degradation Data. R package version 1.2.10, https://pkgdown.jrwb.de/mkin/.","code":"@Manual{,   title = {mkin: Kinetic Evaluation of Chemical Degradation Data},   author = {Johannes Ranke},   year = {2025},   note = {R package version 1.2.10},   url = {https://pkgdown.jrwb.de/mkin/}, }"},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"mkin","dir":"","previous_headings":"","what":"Kinetic Evaluation of Chemical Degradation Data","title":"Kinetic Evaluation of Chemical Degradation Data","text":"R package mkin provides calculation routines analysis chemical degradation data, including multicompartment kinetics needed modelling formation decline transformation products, several degradation compartments involved. provides stable functionality kinetic evaluations according FOCUS guidance (see details). addition, provides functionality hierarchical kinetics based nonlinear mixed-effects models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Kinetic Evaluation of Chemical Degradation Data","text":"can install latest released version CRAN within R:","code":"install.packages(\"mkin\")"},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"background","dir":"","previous_headings":"","what":"Background","title":"Kinetic Evaluation of Chemical Degradation Data","text":"regulatory evaluation chemical substances like plant protection products (pesticides), biocides chemicals, degradation data play important role. evaluation pesticide degradation experiments, detailed guidance various helpful tools developed detailed ‘Credits historical remarks’ . package aims provide one stop solution degradation kinetics, addressing modellers willing , even prefer work R.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"basic-usage","dir":"","previous_headings":"","what":"Basic usage","title":"Kinetic Evaluation of Chemical Degradation Data","text":"start, look code examples provided plot.mkinfit plot.mmkin, package vignettes FOCUS L FOCUS D.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Kinetic Evaluation of Chemical Degradation Data","text":"HTML documentation latest version released CRAN available jrwb.de github. Documentation development version found ‘dev’ subdirectory. articles section documentation, can also find demonstrations application nonlinear hierarchical models, also known nonlinear mixed-effects models, complex data, including transformation products covariates.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"general","dir":"","previous_headings":"Features","what":"General","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Highly flexible model specification using mkinmod, including equilibrium reactions using single first-order reversible binding (SFORB) model, automatically create two state variables observed variable. Model solution (forward modelling) function mkinpredict performed either using analytical solution case parent degradation simple models involving single transformation product, , eigenvalue based solution simple first-order (SFO) SFORB kinetics used model, using numeric solver deSolve package (default lsoda). usual one-sided t-test significant difference zero shown based estimators untransformed parameters. Summary plotting functions. summary mkinfit object fact full report give enough information able approximately reproduce fit tools. chi-squared error level defined FOCUS kinetics guidance (see ) calculated observed variable. ‘variance variable’ error model often fitted using Iteratively Reweighted Least Squares (IRLS) can specified error_model = \"obs\".","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"unique-in-mkin","dir":"","previous_headings":"Features","what":"Unique in mkin","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Three different error models can selected using argument error_model mkinfit function. two-component error model similar one proposed Rocke Lorenzato can selected using argument error_model = \"tc\". Model comparisons using Akaike Information Criterion (AIC) supported can also used non-constant variance. cases FOCUS chi-squared error level meaningful. default, kinetic rate constants kinetic formation fractions transformed internally using transform_odeparms estimators can reasonably expected follow normal distribution. parameter estimates backtransformed match model definition, confidence intervals calculated standard errors also backtransformed correct scale, include meaningless values like negative rate constants formation fractions adding 1, occur single experiment single defined radiolabel position. metabolite decline phase described well SFO kinetics, SFORB kinetics can used metabolite. Mathematically, SFORB model equivalent DFOP model. However, SFORB model advantage mechanistic interpretation model parameters. Nonlinear mixed-effects models (hierarchical models) can created fits degradation model different datasets compound using nlme.mmkin saem.mmkin methods. Note convergence nlme fits depends quality data. Convergence better simple models data many groups (e.g. soils). saem method uses saemix package backend. Analytical solutions suitable use package implemented parent models important models including one metabolite (SFO-SFO DFOP-SFO). Fitting models saem.mmkin, makes use compiled ODE models mkin provides, longer run times (couple minutes hour).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"performance","dir":"","previous_headings":"Features","what":"Performance","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Parallel fitting several models several datasets supported, see example plot.mmkin. C compiler installed, kinetic models compiled automatically generated C code, see vignette compiled_models. autogeneration C code inspired ccSolve package. Thanks Karline Soetaert work . Even compiler installed, many degradation models still give good performance, current versions mkin also analytical solutions models one metabolite, SFO SFORB used parent compound, Eigenvalue based solutions degradation model available.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"gui","dir":"","previous_headings":"","what":"GUI","title":"Kinetic Evaluation of Chemical Degradation Data","text":"graphical user interface may useful. Please refer documentation page installation instructions manual. supports evaluations using (generalised) nonlinear regression, simultaneous fits using nonlinear mixed-effects models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"news","dir":"","previous_headings":"","what":"News","title":"Kinetic Evaluation of Chemical Degradation Data","text":"list changes latest CRAN release one github branch, e.g. main branch.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"credits-and-historical-remarks","dir":"","previous_headings":"","what":"Credits and historical remarks","title":"Kinetic Evaluation of Chemical Degradation Data","text":"mkin possible without underlying software stack consisting , among others, R package deSolve. previous version, mkin also using functionality FME package. Please refer package page CRAN full list imported suggested R packages. Also, Debian Linux, vim editor Nvim-R plugin invaluable development. mkin written without introduced regulatory fate modelling pesticides Adrian Gurney time Harlan Laboratories Ltd (formerly RCC Ltd). mkin greatly profits largely follows work done FOCUS Degradation Kinetics Workgroup, detailed guidance document 2006, slightly updated 2011 2014. Also, inspired first version KinGUI developed BayerCropScience, based MatLab runtime environment. companion package kinfit (now deprecated) started 2008 first published CRAN 01 May 2010. first mkin code published 11 May 2010 first CRAN version 18 May 2010. 2011, Bayer Crop Science started distribute R based successor KinGUI named KinGUII whose R code based mkin, added, among refinements, closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation variance observed variables, Markov Chain Monte Carlo (MCMC) simulation functionality, similar available e.g. FME package. Somewhat parallel, Syngenta sponsored development mkin KinGUII based GUI application called CAKE, also adds IRLS MCMC, limited model formulation, puts weight usability. CAKE available download CAKE website, can also find zip archive R scripts derived mkin, published GPL license. Finally, KineticEval, contains development scripts used KinGUII. Thanks René Lehmann, formerly working Umweltbundesamt, nice cooperation parameter transformations, especially isometric log-ratio transformation now used formation fractions case two transformation targets. Many inspirations improvements mkin resulted kinetic evaluations degradation data clients working Harlan Laboratories Eurofins Regulatory AG, now independent consultant. Funding received Umweltbundesamt course projects Project Number 27452 (Testing validation modelling software alternative ModelMaker 4.0, 2014-2015) Project Number 56703 (Optimization gmkin routine use Umweltbundesamt, 2015) Project Number 92570 (Update Project Number 27452, 2017-2018) Project Number 112407 (Testing feasibility using error model according Rocke Lorenzato realistic parameter estimates kinetic evaluation degradation data, 2018-2019) Project Number 120667 (Development objective criteria evaluation visual fit kinetic evaluation degradation data, 2019-2020) Project Number 146839 (Checking feasibility using mixed-effects models derivation kinetic modelling parameters degradation studies, 2020-2021) Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) Thanks everyone involved collaboration support! Thanks due also Emmanuelle Comets, maintainer saemix package, interest support using SAEM algorithm implementation saemix evaluation chemical degradation data. Regarding application nonlinear mixed-effects models degradation data, von Götz et al (1999) already proposed use technique context environmental risk assessments pesticides. However, work apparently followed , independently arrive idea missed cite previous work topic first publications.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"development","dir":"","previous_headings":"","what":"Development","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Contributions welcome!","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"Provides convenient way compare different kinetic models fitted dataset.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"","code":"# S3 method for class 'mmkin' AIC(object, ..., k = 2)  # S3 method for class 'mmkin' BIC(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"object object class mmkin, containing one column. ... compatibility generic method k generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"generic method (numeric value single fits, dataframe several fits column).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"","code":"# skip, as it takes > 10 s on winbuilder   f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"),     list(\"FOCUS A\" = FOCUS_2006_A,          \"FOCUS C\" = FOCUS_2006_C), cores = 1, quiet = TRUE) #> Warning: Optimisation did not converge: #> false convergence (8)   # We get a warning because the FOMC model does not converge for the   # FOCUS A dataset, as it is well described by SFO    AIC(f[\"SFO\", \"FOCUS A\"]) # We get a single number for a single fit #> [1] 55.28197   AIC(f[[\"SFO\", \"FOCUS A\"]]) # or when extracting an mkinfit object #> [1] 55.28197    # For FOCUS A, the models fit almost equally well, so the higher the number   # of parameters, the higher (worse) the AIC   AIC(f[, \"FOCUS A\"]) #>      df      AIC #> SFO   3 55.28197 #> FOMC  4 57.28198 #> DFOP  5 59.28197   AIC(f[, \"FOCUS A\"], k = 0) # If we do not penalize additional parameters, we get nearly the same #>      df      AIC #> SFO   3 49.28197 #> FOMC  4 49.28198 #> DFOP  5 49.28197   BIC(f[, \"FOCUS A\"])        # Comparing the BIC gives a very similar picture #>      df      BIC #> SFO   3 55.52030 #> FOMC  4 57.59974 #> DFOP  5 59.67918    # For FOCUS C, the more complex models fit better   AIC(f[, \"FOCUS C\"]) #>      df      AIC #> SFO   3 59.29336 #> FOMC  4 44.68652 #> DFOP  5 29.02372   BIC(f[, \"FOCUS C\"]) #>      df      BIC #> SFO   3 59.88504 #> FOMC  4 45.47542 #> DFOP  5 30.00984"},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Export a list of datasets format to a CAKE study file — CAKE_export","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"addition datasets, pathways degradation model can specified well.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"","code":"CAKE_export(   ds,   map = c(parent = \"Parent\"),   links = NA,   filename = \"CAKE_export.csf\",   path = \".\",   overwrite = FALSE,   study = \"Degradinol aerobic soil degradation\",   description = \"\",   time_unit = \"days\",   res_unit = \"% AR\",   comment = \"\",   date = Sys.Date(),   optimiser = \"IRLS\" )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"ds named list datasets long format compatible mkinfit. map character vector CAKE compartment names (Parent, A1, ...), named names used list datasets. links optional character vector target compartments, named names source compartments. order make easier, names used datasets supplied. filename write result. end .csf order compatible CAKE. path optional path output file. overwrite TRUE, existing files overwritten. study name study. description optional description. time_unit time unit residue data. res_unit unit used residues. comment optional comment. date date file creation. optimiser Can OLS IRLS.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"five datasets extracted active substance evaluation dossier published EFSA. Kinetic evaluations shown datasets intended illustrate advance kinetic modelling. fact data results shown imply license use context pesticide  registrations, use data may constrained data protection regulations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"","code":"D24_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"mkindsg object grouping five datasets","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"Hellenic Ministry Rural Development Agriculture (2014) Final addendum Renewal Assessment Report - public version - 2,4-D Volume 3 Annex B.8 Fate behaviour environment https://open.efsa.europa.eu/study-inventory/EFSA-Q-2013-00811","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"Data first dataset p. 685. Data four datasets used preprocessed versions given kinetics section (p. 761ff.), exception residues smaller 1 DCP soil Site I2, values given p. 694 used. R code used create data object installed package 'dataset_generation' directory. code, page numbers given specific pieces information comments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"","code":"print(D24_2014) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Aerobic soil degradation data on 2,4-D from the EU assessment in 2014  #> Occurrence of observed compounds $observed_n: #> D24 DCP DCA  #>   5   4   4  #> Time normalisation factors $f_time_norm: #> [1] 1.6062378 0.7118732 0.7156063 0.7156063 0.8977124 #> Meta information $meta: #>                                 study usda_soil_type study_moisture_ref_type #> Mississippi                Cohen 1991      Silt loam                    <NA> #> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1 #> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1 #> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1 #> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1 #>             rel_moisture temperature #> Mississippi           NA          25 #> Fayette              0.5          20 #> RefSol 03-G          0.5          20 #> Site E1              0.5          20 #> Site I2              0.5          20 # \\dontrun{ print(D24_2014$ds[[1]], data = TRUE) #> <mkinds> with $title:  Mississippi  #> Observed compounds $observed:  D24  #> Sampling times $sampling_times: #> 0, 2, 4, 7, 15, 24, 35, 56, 71, 114, 183, 273, 365  #> With a maximum of  1  replicates #>    time  D24 #> 1     0 96.8 #> 2     2 81.0 #> 3     4 81.7 #> 4     7 88.2 #> 5    15 66.3 #> 6    24 72.9 #> 7    35 62.6 #> 8    56 54.6 #> 9    71 35.2 #> 10  114 18.0 #> 11  183 11.3 #> 12  273  9.9 #> 13  365  6.3 m_D24 = mkinmod(D24 = mkinsub(\"SFO\", to = \"DCP\"),   DCP = mkinsub(\"SFO\", to = \"DCA\"),   DCA = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(m_D24) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $D24 #> $type: SFO; $to: DCP; $sink: TRUE #> $DCP #> $type: SFO; $to: DCA; $sink: TRUE #> $DCA #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_D24/dt = - k_D24 * D24 #> d_DCP/dt = + f_D24_to_DCP * k_D24 * D24 - k_DCP * DCP #> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA m_D24_2 = mkinmod(D24 = mkinsub(\"DFOP\", to = \"DCP\"),   DCP = mkinsub(\"SFO\", to = \"DCA\"),   DCA = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(m_D24_2) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $D24 #> $type: DFOP; $to: DCP; $sink: TRUE #> $DCP #> $type: SFO; $to: DCA; $sink: TRUE #> $DCA #> $type: SFO; $sink: TRUE #> Compiled model $cf available #> Differential equations: #> d_D24/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * D24 #> d_DCP/dt = + f_D24_to_DCP * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * #>            exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 #>            * time))) * D24 - k_DCP * DCP #> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA D24_2014_data <- lapply(D24_2014$ds, function(x) x$data) names(D24_2014_data) <- sapply(D24_2014$ds, function(x) x$title) f_D24_2014 <- mmkin(   models = list(     \"SFO-SFO-SFO\" = m_D24,      \"DFOP-SFO-SFO\" = m_D24_2),   data = D24_2014_data,   quiet = TRUE) print(f_D24_2014) #> <mmkin> object #> Status of individual fits: #>  #>               dataset #> model          Mississippi Fayette RefSol 03-G Site E1 Site I2 #>   SFO-SFO-SFO  OK          Z       Z           Z       Z       #>   DFOP-SFO-SFO OK          Z       Z           Z       Z       #>  #> Z: Observations with value of zero were removed from the data #> OK: No warnings plot(f_D24_2014[, 3])  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Double First-Order in Parallel kinetics — DFOP.solution","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"Function describing decline defined starting value using sum two exponential decline functions.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"","code":"DFOP.solution(t, parent_0, k1, k2, g)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"t Time. parent_0 Starting value response variable time zero. k1 First kinetic constant. k2 Second kinetic constant. g Fraction starting value declining according first kinetic constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"","code":"plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Subsetting method for mmkin objects — [.mmkin","title":"Subsetting method for mmkin objects — [.mmkin","text":"Subsetting method mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subsetting method for mmkin objects — [.mmkin","text":"","code":"# S3 method for class 'mmkin' x[i, j, ..., drop = FALSE]"},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subsetting method for mmkin objects — [.mmkin","text":"x mmkin object Row index selecting fits specific models j Column index selecting fits specific datasets ... used, satisfy generic method definition drop FALSE, method always returns mmkin object, otherwise either list mkinfit objects single mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subsetting method for mmkin objects — [.mmkin","text":"object class mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Subsetting method for mmkin objects — [.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subsetting method for mmkin objects — [.mmkin","text":"","code":"# Only use one core, to pass R CMD check --as-cran   fits <- mmkin(c(\"SFO\", \"FOMC\"), list(B = FOCUS_2006_B, C = FOCUS_2006_C),                 cores = 1, quiet = TRUE)   fits[\"FOMC\", ] #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  B  C  #>   FOMC OK OK #>  #> OK: No warnings   fits[, \"B\"] #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  B  #>   SFO  OK #>   FOMC OK #>  #> OK: No warnings   fits[\"SFO\", \"B\"] #> <mmkin> object #> Status of individual fits: #>  #>      dataset #> model B  #>   SFO OK #>  #> OK: No warnings    head(     # This extracts an mkinfit object with lots of components     fits[[\"FOMC\", \"B\"]]   ) #> $par #>  parent_0 log_alpha  log_beta     sigma  #> 99.666192  2.549850  5.050587  1.890202  #>  #> $objective #> [1] 28.58291 #>  #> $convergence #> [1] 0 #>  #> $iterations #> [1] 21 #>  #> $evaluations #> function gradient  #>       25       78  #>  #> $message #> [1] \"both X-convergence and relative convergence (5)\" #>"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"","code":"FOCUS_2006_DFOP_ref_A_to_B"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound f fitted f parameter k1 fitted k1 parameter k2 fitted k2 parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"","code":"data(FOCUS_2006_DFOP_ref_A_to_B)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"","code":"FOCUS_2006_FOMC_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound alpha fitted alpha parameter beta fitted beta parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"","code":"data(FOCUS_2006_FOMC_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"","code":"FOCUS_2006_HS_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound tb fitted tb parameter k1 fitted k1 parameter k2 fitted k2 parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"","code":"data(FOCUS_2006_HS_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"","code":"FOCUS_2006_SFO_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound k fitted first-order degradation rate constant DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"","code":"data(FOCUS_2006_SFO_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"Data taken FOCUS (2006), p. 258.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"","code":"FOCUS_2006_A   FOCUS_2006_B   FOCUS_2006_C   FOCUS_2006_D   FOCUS_2006_E   FOCUS_2006_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"6 datasets observations following variables. name factor containing name observed variable time numeric vector containing time points value numeric vector containing concentrations percent applied radioactivity","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"","code":"FOCUS_2006_C #>     name time value #> 1 parent    0  85.1 #> 2 parent    1  57.9 #> 3 parent    3  29.9 #> 4 parent    7  14.6 #> 5 parent   14   9.7 #> 6 parent   28   6.6 #> 7 parent   63   4.0 #> 8 parent   91   3.9 #> 9 parent  119   0.6"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"First-Order Multi-Compartment kinetics — FOMC.solution","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"Function describing exponential decline defined starting value, decreasing rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"","code":"FOMC.solution(t, parent_0, alpha, beta)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"t Time. parent_0 Starting value response variable time zero. alpha Shape parameter determined coefficient variation rate constant values. beta Location parameter.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"form given differs slightly original reference Gustafson Holden (1990). parameter beta corresponds 1/beta original equation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"solution FOMC kinetic model reduces SFO.solution large values alpha beta \\(k = \\frac{\\beta}{\\alpha}\\).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics Gustafson DI Holden LR (1990) Nonlinear pesticide dissipation soil: new model based spatial variability. Environmental Science Technology 24, 1032-1038","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"","code":"plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Hockey-Stick kinetics — HS.solution","title":"Hockey-Stick kinetics — HS.solution","text":"Function describing two exponential decline functions break point .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hockey-Stick kinetics — HS.solution","text":"","code":"HS.solution(t, parent_0, k1, k2, tb)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hockey-Stick kinetics — HS.solution","text":"t Time. parent_0 Starting value response variable time zero. k1 First kinetic constant. k2 Second kinetic constant. tb Break point. time, exponential decline according k1 calculated, time, exponential decline proceeds according k2.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hockey-Stick kinetics — HS.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hockey-Stick kinetics — HS.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hockey-Stick kinetics — HS.solution","text":"","code":"plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Indeterminate order rate equation kinetics — IORE.solution","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"Function describing exponential decline defined starting value, concentration dependent rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"","code":"IORE.solution(t, parent_0, k__iore, N)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"t Time. parent_0 Starting value response variable time zero. k__iore Rate constant. Note depends concentration units used. N Exponent describing nonlinearity rate equation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"solution IORE kinetic model reduces SFO.solution N = 1.  parameters IORE model can transformed equivalent parameters FOMC mode - see NAFTA guidance details.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"NAFTA Technical Working Group Pesticides (dated) Guidance Evaluating Calculating Degradation Kinetics Environmental Media","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"","code":"plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))    # \\dontrun{     fit.fomc <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)     fit.iore <- mkinfit(\"IORE\", FOCUS_2006_C, quiet = TRUE)     fit.iore.deS <- mkinfit(\"IORE\", FOCUS_2006_C, solution_type = \"deSolve\", quiet = TRUE) #> Error in is.loaded(initfunc, PACKAGE = dllname, type = \"\") :  #>   invalid 'PACKAGE' argument      print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par,                      row.names = paste(\"model par\", 1:4))) #>             fit.fomc.par fit.iore.par fit.iore.deS.par #> model par 1  85.87489063    85.874890        85.874890 #> model par 2   0.05192238    -4.826631        -4.826631 #> model par 3   0.65096665     1.949403         1.949403 #> model par 4   1.85744396     1.857444         1.857444     print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,                 iore.deS = endpoints(fit.iore)$distimes)) #>              DT50    DT90 DT50back #> fomc     1.785233 15.1479 4.559973 #> iore     1.785233 15.1479 4.559973 #> iore.deS 1.785233 15.1479 4.559973   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":null,"dir":"Reference","previous_headings":"","what":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"Data taken US EPA (2015), p. 19 23.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"","code":"NAFTA_SOP_Appendix_B   NAFTA_SOP_Appendix_D"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"2 datasets observations following variables. name factor containing name observed variable time numeric vector containing time points value numeric vector containing concentrations","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics   environmental media. NAFTA Technical Working Group Pesticides   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation   accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance   Calculate Representative Half-life Values Characterizing Pesticide   Degradation   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"","code":"nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> 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   print(nafta_evaluation) #> Sums of squares: #>       SFO      IORE      DFOP  #> 1378.6832  615.7730  517.8836  #>  #> Critical sum of squares for checking the SFO model: #> [1] 717.4598 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)    Lower   Upper #> parent_0  83.7558 1.80e-14 77.18268 90.3288 #> k_parent   0.0017 7.43e-05  0.00112  0.0026 #> sigma      8.7518 1.22e-05  5.64278 11.8608 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       9.69e+01     NA 8.88e+01 1.05e+02 #> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09 #> N_parent       6.68e+00     NA 4.19e+00 9.17e+00 #> sigma          5.85e+00     NA 3.76e+00 7.94e+00 #>  #> $DFOP #>          Estimate   Pr(>t)    Lower    Upper #> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 #> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02 #> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03 #> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01 #> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00 #>  #>  #> DTx values: #>      DT50    DT90 DT50_rep #> SFO   407    1350      407 #> IORE  541 5190000  1560000 #> DFOP  429    2380      841 #>  #> Representative half-life: #> [1] 841.41   plot(nafta_evaluation)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":null,"dir":"Reference","previous_headings":"","what":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"Data taken Attachment 1 SOP.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"","code":"NAFTA_SOP_Attachment"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"list (NAFTA_SOP_Attachment) containing 16 datasets suitable   evaluation nafta","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics   environmental media. NAFTA Technical Working Group Pesticides   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation   accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance   Calculate Representative Half-life Values Characterizing Pesticide   Degradation   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"","code":"nafta_att_p5a <- nafta(NAFTA_SOP_Attachment[[\"p5a\"]], cores = 1) #> 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   print(nafta_att_p5a) #> Sums of squares: #>       SFO      IORE      DFOP  #> 465.21753  56.27506  32.06401  #>  #> Critical sum of squares for checking the SFO model: #> [1] 64.4304 #>  #> Parameters: #> $SFO #>          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 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 #> g        6.47e-01 3.67e-06  0.6248   0.6677 #> sigma    1.27e+00 8.91e-06  0.8395   1.6929 #>  #>  #> DTx values: #>      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 #>  #> Representative half-life: #> [1] 321.51   plot(nafta_att_p5a)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Single First-Order kinetics — SFO.solution","title":"Single First-Order kinetics — SFO.solution","text":"Function describing exponential decline defined starting value.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single First-Order kinetics — SFO.solution","text":"","code":"SFO.solution(t, parent_0, k)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single First-Order kinetics — SFO.solution","text":"t Time. parent_0 Starting value response variable time zero. k Kinetic rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single First-Order kinetics — SFO.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single First-Order kinetics — SFO.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single First-Order kinetics — SFO.solution","text":"","code":"plot(function(x) SFO.solution(x, 100, 3), 0, 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Single First-Order Reversible Binding kinetics — SFORB.solution","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"Function describing solution differential equations describing kinetic model first-order terms two-way transfer free bound fraction, first-order degradation term free fraction.  initial condition defined amount free fraction substance bound fraction.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"","code":"SFORB.solution(t, parent_0, k_12, k_21, k_1output)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"t Time. parent_0 Starting value response variable time zero. k_12 Kinetic constant describing transfer free bound. k_21 Kinetic constant describing transfer bound free. k_1output Kinetic constant describing degradation free fraction.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"value response variable, sum free bound fractions time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"","code":"plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":null,"dir":"Reference","previous_headings":"","what":"Add normally distributed errors to simulated kinetic degradation data — add_err","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Normally distributed errors added data predicted specific degradation model using mkinpredict. variance error may depend predicted value specified standard deviation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"","code":"add_err(   prediction,   sdfunc,   secondary = c(\"M1\", \"M2\"),   n = 10,   LOD = 0.1,   reps = 2,   digits = 1,   seed = NA )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"prediction prediction kinetic model produced mkinpredict. sdfunc function taking predicted value argument returning standard deviation used generating random error terms value. secondary names state variables initial value zero n number datasets generated. LOD limit detection (LOD). Values LOD adding random error set NA. reps number replicates generated within datasets. digits number digits values rounded. seed seed used generation random numbers. NA, seed set.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"list datasets compatible mmkin, .e. components list datasets compatible mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Ranke J Lehmann R (2015) t-test t-test, question. XV Symposium Pesticide Chemistry 2-4 September 2015, Piacenza, Italy https://jrwb.de/posters/piacenza_2015.pdf","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"","code":"# The kinetic model m_SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                      M1 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  # Generate a prediction for a specific set of parameters sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)  # This is the prediction used for the \"Type 2 datasets\" on the Piacenza poster # from 2015 d_SFO_SFO <- mkinpredict(m_SFO_SFO,                          c(k_parent = 0.1, f_parent_to_M1 = 0.5,                            k_M1 = log(2)/1000),                          c(parent = 100, M1 = 0),                          sampling_times)  # Add an error term with a constant (independent of the value) standard deviation # of 10, and generate three datasets d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )  # Name the datasets for nicer plotting names(d_SFO_SFO_err) <- paste(\"Dataset\", 1:3)  # Name the model in the list of models (with only one member in this case) for # nicer plotting later on.  Be quiet and use only one core not to offend CRAN # checks # \\dontrun{ f_SFO_SFO <- mmkin(list(\"SFO-SFO\" = m_SFO_SFO),                    d_SFO_SFO_err, cores = 1,                    quiet = TRUE)  plot(f_SFO_SFO)   # We would like to inspect the fit for dataset 3 more closely # Using double brackets makes the returned object an mkinfit object # instead of a list of mkinfit objects, so plot.mkinfit is used plot(f_SFO_SFO[[3]], show_residuals = TRUE)   # If we use single brackets, we should give two indices (model and dataset), # and plot.mmkin is used plot(f_SFO_SFO[1, 3])  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Anova method for saem.mmkin objects — anova.saem.mmkin","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"Generate anova object. method calculate BIC saemix package. prominent anova methods, models sorted number parameters, tests (requested) always relative model previous line.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' anova(   object,   ...,   method = c(\"is\", \"lin\", \"gq\"),   test = FALSE,   model.names = NULL )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"object saem.mmkin object ... objects method Method likelihood calculation: \"\" (importance sampling), \"lin\" (linear approximation), \"gq\" (Gaussian quadrature). Passed saemix::logLik.SaemixObject test likelihood ratio test performed? TRUE, alternative models tested first model. done nested models. model.names Optional character vector model names","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"\"anova\" data frame; traditional (S3) result anova()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike weights for model averaging — aw","title":"Calculate Akaike weights for model averaging — aw","text":"Akaike weights calculated based relative expected Kullback-Leibler information specified Burnham Anderson (2004).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike weights for model averaging — aw","text":"","code":"aw(object, ...)  # S3 method for class 'mkinfit' aw(object, ...)  # S3 method for class 'mmkin' aw(object, ...)  # S3 method for class 'mixed.mmkin' aw(object, ...)  # S3 method for class 'multistart' aw(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike weights for model averaging — aw","text":"object mmkin column object, containing two mkinfit models fitted data, mkinfit object. latter case, mkinfit objects fitted data specified dots arguments. ... used method mmkin column objects, mkinfit objects method mkinfit objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Akaike weights for model averaging — aw","text":"Burnham KP Anderson DR (2004) Multimodel Inference: Understanding AIC BIC Model Selection. Sociological Methods & Research 33(2) 261-304","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike weights for model averaging — aw","text":"","code":"# \\dontrun{ f_sfo <- mkinfit(\"SFO\", FOCUS_2006_D, quiet = TRUE) f_dfop <- mkinfit(\"DFOP\", FOCUS_2006_D, quiet = TRUE) aw_sfo_dfop <- aw(f_sfo, f_dfop) sum(aw_sfo_dfop) #> [1] 1 aw_sfo_dfop # SFO gets more weight as it has less parameters and a similar fit #> [1] 0.5970258 0.4029742 f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), list(\"FOCUS D\" = FOCUS_2006_D), cores = 1, quiet = TRUE) aw(f) #> [1] 0.4808722 0.1945539 0.3245740 sum(aw(f)) #> [1] 1 aw(f[c(\"SFO\", \"DFOP\")]) #> [1] 0.5970258 0.4029742 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if fit within an mhmkin object failed — check_failed","title":"Check if fit within an mhmkin object failed — check_failed","text":"Check fit within mhmkin object failed","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if fit within an mhmkin object failed — check_failed","text":"","code":"check_failed(x)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if fit within an mhmkin object failed — check_failed","text":"x object checked","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"default method 'quadratic' based quadratic approximation curvature likelihood function maximum likelihood parameter estimates. alternative method 'profile' based profile likelihood parameter. 'profile' method uses two nested optimisations can take long time, even parallelized specifying 'cores' unixoid platforms. speed method likely improved using method Venzon Moolgavkar (1988).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"","code":"# S3 method for class 'mkinfit' confint(   object,   parm,   level = 0.95,   alpha = 1 - level,   cutoff,   method = c(\"quadratic\", \"profile\"),   transformed = TRUE,   backtransform = TRUE,   cores = parallel::detectCores(),   rel_tol = 0.01,   quiet = FALSE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"object mkinfit object parm vector names parameters given confidence intervals. missing, parameters considered. level confidence level required alpha allowed error probability, overrides 'level' specified. cutoff Possibility specify alternative cutoff difference log-likelihoods confidence boundary. Specifying explicit cutoff value overrides arguments 'level' 'alpha' method 'quadratic' method approximates likelihood function optimised parameters using second term Taylor expansion, using second derivative (hessian) contained object. 'profile' method searches parameter space cutoff confidence intervals means likelihood ratio test. transformed quadratic approximation used, applied likelihood based transformed parameters? backtransform approximate likelihood terms transformed parameters, backtransform parameters confidence intervals? cores number cores used multicore processing. Windows machines, cores > 1 currently supported. rel_tol method 'profile', accuracy lower upper bounds, relative estimate obtained quadratic method? quiet suppress message \"Profiling likelihood\" ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"matrix columns giving lower upper confidence limits parameter.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"Bates DM Watts GW (1988) Nonlinear regression analysis & applications Pawitan Y (2013) likelihood - Statistical modelling inference using likelihood. Clarendon Press, Oxford. Venzon DJ Moolgavkar SH (1988) Method Computing Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, 87–94.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"","code":"f <- mkinfit(\"SFO\", FOCUS_2006_C, quiet = TRUE) confint(f, method = \"quadratic\") #>                2.5%      97.5% #> parent_0 71.8242430 93.1600766 #> k_parent  0.2109541  0.4440528 #> sigma     1.9778868  7.3681380  # \\dontrun{ confint(f, method = \"profile\") #> Profiling the likelihood #>                2.5%      97.5% #> parent_0 73.0641834 92.1392181 #> k_parent  0.2170293  0.4235348 #> sigma     3.1307772  8.0628314  # Set the number of cores for the profiling method for further examples if (identical(Sys.getenv(\"NOT_CRAN\"), \"true\")) {   n_cores <- parallel::detectCores() - 1 } else {   n_cores <- 1 } if (Sys.getenv(\"TRAVIS\") != \"\") n_cores = 1 if (Sys.info()[\"sysname\"] == \"Windows\") n_cores = 1  SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\"),   use_of_ff = \"min\", quiet = TRUE) SFO_SFO.ff <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\"),   use_of_ff = \"max\", quiet = TRUE) f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE) system.time(ci_profile <- confint(f_d_1, method = \"profile\", cores = 1, quiet = TRUE)) #>    user  system elapsed  #>   1.194   0.000   1.194  # Using more cores does not save much time here, as parent_0 takes up most of the time # If we additionally exclude parent_0 (the confidence of which is often of # minor interest), we get a nice performance improvement if we use at least 4 cores system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = \"profile\",   c(\"k_parent_sink\", \"k_parent_m1\", \"k_m1_sink\", \"sigma\"), cores = n_cores)) #> Profiling the likelihood #>    user  system elapsed  #>   0.439   0.119   0.304  ci_profile #>                       2.5%        97.5% #> parent_0      96.456003640 1.027703e+02 #> k_parent_sink  0.040762501 5.549764e-02 #> k_parent_m1    0.046786482 5.500879e-02 #> k_m1_sink      0.003892605 6.702778e-03 #> sigma          2.535612399 3.985263e+00 ci_quadratic_transformed <- confint(f_d_1, method = \"quadratic\") ci_quadratic_transformed #>                       2.5%        97.5% #> parent_0      96.403841640 1.027931e+02 #> k_parent_sink  0.041033378 5.596269e-02 #> k_parent_m1    0.046777902 5.511931e-02 #> k_m1_sink      0.004012217 6.897547e-03 #> sigma          2.396089689 3.854918e+00 ci_quadratic_untransformed <- confint(f_d_1, method = \"quadratic\", transformed = FALSE) ci_quadratic_untransformed #>                       2.5%        97.5% #> parent_0      96.403841645 102.79312449 #> k_parent_sink  0.040485331   0.05535491 #> k_parent_m1    0.046611582   0.05494364 #> k_m1_sink      0.003835483   0.00668582 #> sigma          2.396089689   3.85491806 # Against the expectation based on Bates and Watts (1988), the confidence # intervals based on the internal parameter transformation are less # congruent with the likelihood based intervals. Note the superiority of the # interval based on the untransformed fit for k_m1_sink rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile) rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile) rel_diffs_transformed < rel_diffs_untransformed #>                2.5% 97.5% #> parent_0      FALSE FALSE #> k_parent_sink  TRUE FALSE #> k_parent_m1    TRUE FALSE #> k_m1_sink     FALSE FALSE #> sigma         FALSE FALSE signif(rel_diffs_transformed, 3) #>                   2.5%    97.5% #> parent_0      0.000541 0.000222 #> k_parent_sink 0.006650 0.008380 #> k_parent_m1   0.000183 0.002010 #> k_m1_sink     0.030700 0.029100 #> sigma         0.055000 0.032700 signif(rel_diffs_untransformed, 3) #>                   2.5%    97.5% #> parent_0      0.000541 0.000222 #> k_parent_sink 0.006800 0.002570 #> k_parent_m1   0.003740 0.001180 #> k_m1_sink     0.014700 0.002530 #> sigma         0.055000 0.032700   # Investigate a case with formation fractions f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE) ci_profile_ff <- confint(f_d_2, method = \"profile\", cores = n_cores) #> Profiling the likelihood ci_profile_ff #>                        2.5%        97.5% #> parent_0       96.456003640 1.027703e+02 #> k_parent        0.090911032 1.071578e-01 #> k_m1            0.003892606 6.702775e-03 #> f_parent_to_m1  0.471328495 5.611550e-01 #> sigma           2.535612399 3.985263e+00 ci_quadratic_transformed_ff <- confint(f_d_2, method = \"quadratic\") ci_quadratic_transformed_ff #>                        2.5%        97.5% #> parent_0       96.403833581 102.79311649 #> k_parent        0.090823771   0.10725430 #> k_m1            0.004012219   0.00689755 #> f_parent_to_m1  0.469118824   0.55959615 #> sigma           2.396089689   3.85491806 ci_quadratic_untransformed_ff <- confint(f_d_2, method = \"quadratic\", transformed = FALSE) ci_quadratic_untransformed_ff #>                        2.5%        97.5% #> parent_0       96.403833586 1.027931e+02 #> k_parent        0.090491913 1.069035e-01 #> k_m1            0.003835485 6.685823e-03 #> f_parent_to_m1  0.469113477 5.598387e-01 #> sigma           2.396089689 3.854918e+00 rel_diffs_transformed_ff <- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff) rel_diffs_untransformed_ff <- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff) # While the confidence interval for the parent rate constant is closer to # the profile based interval when using the internal parameter # transformation, the interval for the metabolite rate constant is 'better # without internal parameter transformation. rel_diffs_transformed_ff < rel_diffs_untransformed_ff #>                 2.5% 97.5% #> parent_0       FALSE FALSE #> k_parent        TRUE  TRUE #> k_m1           FALSE FALSE #> f_parent_to_m1  TRUE FALSE #> sigma           TRUE FALSE rel_diffs_transformed_ff #>                        2.5%        97.5% #> parent_0       0.0005408690 0.0002217233 #> k_parent       0.0009598532 0.0009001864 #> k_m1           0.0307283045 0.0290588367 #> f_parent_to_m1 0.0046881768 0.0027780062 #> sigma          0.0550252516 0.0327066836 rel_diffs_untransformed_ff #>                        2.5%        97.5% #> parent_0       0.0005408689 0.0002217233 #> k_parent       0.0046102155 0.0023732280 #> k_m1           0.0146740687 0.0025291815 #> f_parent_to_m1 0.0046995210 0.0023457712 #> sigma          0.0550252516 0.0327066836  # The profiling for the following fit does not finish in a reasonable time, # therefore we use the quadratic approximation m_synth_DFOP_par <- mkinmod(parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),   M1 = mkinsub(\"SFO\"),   M2 = mkinsub(\"SFO\"),   use_of_ff = \"max\", quiet = TRUE) DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = \"tc\",   error_model_algorithm = \"direct\", quiet = TRUE) confint(f_tc_2, method = \"quadratic\") #>                        2.5%        97.5% #> parent_0       94.596181875 106.19936592 #> k_M1            0.037605432   0.04490757 #> k_M2            0.008568745   0.01087675 #> f_parent_to_M1  0.021464676   0.62023880 #> f_parent_to_M2  0.015167158   0.37975350 #> k1              0.273897535   0.33388072 #> k2              0.018614555   0.02250379 #> g               0.671943738   0.73583261 #> sigma_low       0.251283679   0.83992102 #> rsd_high        0.040411022   0.07662008 confint(f_tc_2, \"parent_0\", method = \"quadratic\") #>              2.5%    97.5% #> parent_0 94.59618 106.1994 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":null,"dir":"Reference","previous_headings":"","what":"Create degradation functions for known analytical solutions — create_deg_func","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"Create degradation functions known analytical solutions","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"","code":"create_deg_func(spec, use_of_ff = c(\"min\", \"max\"))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"spec List model specifications contained mkinmod objects use_of_ff Minimum maximum use formation fractions","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"Degradation function attached mkinmod objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"","code":"SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) # to avoid warnings fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE) # \\dontrun{ fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE) if (require(rbenchmark))   benchmark(     analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE),     deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE),     replications = 2) #> Loading required package: rbenchmark #>         test replications elapsed relative user.self sys.self user.child #> 1 analytical            2   0.239    1.000     0.239        0          0 #> 2    deSolve            2   0.297    1.243     0.297        0          0 #>   sys.child #> 1         0 #> 2         0   DFOP_SFO <- mkinmod(     parent = mkinsub(\"DFOP\", \"m1\"),     m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded   benchmark(     analytical = mkinfit(DFOP_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE),     deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE),     replications = 2) #>         test replications elapsed relative user.self sys.self user.child #> 1 analytical            2   0.389    1.000     0.388        0          0 #> 2    deSolve            2   0.513    1.319     0.513        0          0 #>   sys.child #> 1         0 #> 2         0 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":null,"dir":"Reference","previous_headings":"","what":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"datasets extracted active substance evaluation dossier published EFSA. Kinetic evaluations shown datasets intended illustrate advance kinetic modelling. fact data results shown imply license use context pesticide  registrations, use data may constrained data protection regulations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"","code":"dimethenamid_2018"},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"mkindsg object grouping seven datasets meta information","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate behaviour Rev. 2 - November 2017 https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"R code used create data object installed package 'dataset_generation' directory. code, page numbers given specific pieces information comments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"","code":"print(dimethenamid_2018) #> <mkindsg> holding 7 mkinds objects #> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018  #> Occurrence of observed compounds $observed_n: #> DMTAP   M23   M27   M31  DMTA  #>     3     7     7     7     4  #> Time normalisation factors $f_time_norm: #> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938 #> Meta information $meta: #>                      study  usda_soil_type study_moisture_ref_type rel_moisture #> Calke        Unsworth 2014      Sandy loam                     pF2         1.00 #> Borstel  Staudenmaier 2009            Sand                     pF1         0.50 #> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75 #> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75 #> Flaach          König 1996 Sandy clay loam                     pF1         0.40 #> BBA 2.2         König 1995      Loamy sand                     pF1         0.40 #> BBA 2.3         König 1995      Sandy loam                     pF1         0.40 #>          study_ref_moisture temperature #> Calke                    NA          20 #> Borstel               23.00          20 #> Elliot 1              33.37          23 #> Elliot 2              33.37          23 #> Flaach                   NA          20 #> BBA 2.2                  NA          20 #> BBA 2.3                  NA          20 dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL # \\dontrun{ # We don't use DFOP for the parent compound, as this gives numerical # instabilities in the fits sfo_sfo3p <- mkinmod(  DMTA = mkinsub(\"SFO\", c(\"M23\", \"M27\", \"M31\")),  M23 = mkinsub(\"SFO\"),  M27 = mkinsub(\"SFO\"),  M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),  quiet = TRUE ) dmta_sfo_sfo3p_tc <- mmkin(list(\"SFO-SFO3+\" = sfo_sfo3p),   dmta_ds, error_model = \"tc\", quiet = TRUE) print(dmta_sfo_sfo3p_tc) #> <mmkin> object #> Status of individual fits: #>  #>            dataset #> model       Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot #>   SFO-SFO3+ OK    OK      OK     OK      OK      OK     #>  #> OK: No warnings # The default (test_log_parms = FALSE) gives an undue # influence of ill-defined rate constants that have # extremely small values: plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = FALSE)  # If we disregards ill-defined rate constants, the results # look more plausible, but the truth is likely to be in # between these variants plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE) # We can also specify a default value for the failing # log parameters, to mimic FOCUS guidance plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE,   default_log_parms = log(2)/1000) # As these attempts are not satisfying, we use nonlinear mixed-effects models # f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc) # nlme reaches maxIter = 50 without convergence f_dmta_saem_tc <- saem(dmta_sfo_sfo3p_tc) # I am commenting out the convergence plot as rendering them # with pkgdown fails (at least without further tweaks to the # graphics device used) #saemix::plot(f_dmta_saem_tc$so, plot.type = \"convergence\") summary(f_dmta_saem_tc) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.0  #> Date of fit:     Mon May 12 20:21:22 2025  #> Date of summary: Mon May 12 20:21:22 2025  #>  #> Equations: #> d_DMTA/dt = - k_DMTA * DMTA #> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23 #> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * M27 + k_M31 * M31 #> d_M31/dt = + f_DMTA_to_M31 * k_DMTA * DMTA - k_M31 * M31 #>  #> Data: #> 563 observations of 4 variable(s) grouped in 6 datasets #>  #> Model predictions using solution type deSolve  #>  #> Fitted in 303.226 s #> Using 300, 100 iterations and 9 chains #>  #> Variance model: Two-component variance function  #>  #> Starting values for degradation parameters: #>       DMTA_0   log_k_DMTA    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1  #>      95.5662      -2.9048      -3.8130      -4.1600      -4.1486       0.1341  #> f_DMTA_ilr_2 f_DMTA_ilr_3  #>       0.1385      -1.6700  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>              DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 #> DMTA_0        4.802     0.0000    0.0000     0.000    0.0000       0.0000 #> log_k_DMTA    0.000     0.9834    0.0000     0.000    0.0000       0.0000 #> log_k_M23     0.000     0.0000    0.6983     0.000    0.0000       0.0000 #> log_k_M27     0.000     0.0000    0.0000     1.028    0.0000       0.0000 #> log_k_M31     0.000     0.0000    0.0000     0.000    0.9841       0.0000 #> f_DMTA_ilr_1  0.000     0.0000    0.0000     0.000    0.0000       0.7185 #> f_DMTA_ilr_2  0.000     0.0000    0.0000     0.000    0.0000       0.0000 #> f_DMTA_ilr_3  0.000     0.0000    0.0000     0.000    0.0000       0.0000 #>              f_DMTA_ilr_2 f_DMTA_ilr_3 #> DMTA_0             0.0000       0.0000 #> log_k_DMTA         0.0000       0.0000 #> log_k_M23          0.0000       0.0000 #> log_k_M27          0.0000       0.0000 #> log_k_M31          0.0000       0.0000 #> f_DMTA_ilr_1       0.0000       0.0000 #> f_DMTA_ilr_2       0.7378       0.0000 #> f_DMTA_ilr_3       0.0000       0.4451 #>  #> Starting values for error model parameters: #> a.1 b.1  #>   1   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>    AIC  BIC logLik #>   2276 2273  -1120 #>  #> Optimised parameters: #>                    est.   lower   upper #> DMTA_0          88.4862 84.1127 92.8598 #> log_k_DMTA      -3.0512 -3.5674 -2.5351 #> log_k_M23       -4.0576 -4.9013 -3.2139 #> log_k_M27       -3.8584 -4.2572 -3.4595 #> log_k_M31       -3.9779 -4.4844 -3.4714 #> f_DMTA_ilr_1     0.1264 -0.2186  0.4714 #> f_DMTA_ilr_2     0.1509 -0.2547  0.5565 #> f_DMTA_ilr_3    -1.3891 -1.6962 -1.0819 #> a.1              0.9196  0.8307  1.0085 #> b.1              0.1377  0.1205  0.1549 #> SD.DMTA_0        3.5956 -0.8167  8.0078 #> SD.log_k_DMTA    0.6437  0.2784  1.0091 #> SD.log_k_M23     0.9929  0.3719  1.6139 #> SD.log_k_M27     0.4530  0.1522  0.7537 #> SD.log_k_M31     0.5773  0.1952  0.9595 #> SD.f_DMTA_ilr_1  0.4063  0.1505  0.6621 #> SD.f_DMTA_ilr_2  0.4800  0.1817  0.7783 #> SD.f_DMTA_ilr_3  0.3582  0.1350  0.5814 #>  #> Correlation:  #>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2 #> log_k_DMTA    0.0306                                                     #> log_k_M23    -0.0234 -0.0032                                             #> log_k_M27    -0.0380 -0.0049  0.0041                                     #> log_k_M31    -0.0247 -0.0031  0.0022  0.0817                             #> f_DMTA_ilr_1 -0.0046 -0.0006  0.0425 -0.0438  0.0319                     #> f_DMTA_ilr_2 -0.0008 -0.0002  0.0216 -0.0267 -0.0890 -0.0349             #> f_DMTA_ilr_3 -0.1805 -0.0136  0.0434  0.0791  0.0390 -0.0061    0.0053   #>  #> Random effects: #>                   est.   lower  upper #> SD.DMTA_0       3.5956 -0.8167 8.0078 #> SD.log_k_DMTA   0.6437  0.2784 1.0091 #> SD.log_k_M23    0.9929  0.3719 1.6139 #> SD.log_k_M27    0.4530  0.1522 0.7537 #> SD.log_k_M31    0.5773  0.1952 0.9595 #> SD.f_DMTA_ilr_1 0.4063  0.1505 0.6621 #> SD.f_DMTA_ilr_2 0.4800  0.1817 0.7783 #> SD.f_DMTA_ilr_3 0.3582  0.1350 0.5814 #>  #> Variance model: #>       est.  lower  upper #> a.1 0.9196 0.8307 1.0085 #> b.1 0.1377 0.1205 0.1549 #>  #> Backtransformed parameters: #>                   est.     lower    upper #> DMTA_0        88.48621 84.112654 92.85977 #> k_DMTA         0.04730  0.028230  0.07926 #> k_M23          0.01729  0.007437  0.04020 #> k_M27          0.02110  0.014162  0.03144 #> k_M31          0.01872  0.011283  0.03107 #> f_DMTA_to_M23  0.14551        NA       NA #> f_DMTA_to_M27  0.12169        NA       NA #> f_DMTA_to_M31  0.11062        NA       NA #>  #> Resulting formation fractions: #>               ff #> DMTA_M23  0.1455 #> DMTA_M27  0.1217 #> DMTA_M31  0.1106 #> DMTA_sink 0.6222 #>  #> Estimated disappearance times: #>       DT50   DT90 #> DMTA 14.65  48.68 #> M23  40.09 133.17 #> M27  32.85 109.11 #> M31  37.02 122.97 # As the confidence interval for the random effects of DMTA_0 # includes zero, we could try an alternative model without # such random effects # f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc, #   covariance.model = diag(c(0, rep(1, 7)))) # saemix::plot(f_dmta_saem_tc_2$so, plot.type = \"convergence\") # This does not perform better judged by AIC and BIC # saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","title":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","text":"R code used create data object installed package 'dataset_generation' directory.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","text":"","code":"# \\dontrun{   sfo_mmkin <- mmkin(\"SFO\", ds_sfo, quiet = TRUE, error_model = \"tc\", cores = 15)   sfo_saem <- saem(sfo_mmkin, no_random_effect = \"parent_0\")   plot(sfo_saem)  # }  # This is the code used to generate the datasets cat(readLines(system.file(\"dataset_generation/ds_mixed.R\", package = \"mkin\")), sep = \"\\n\") #> # Synthetic data for hierarchical kinetic models #> # Refactored version of the code previously in tests/testthat/setup_script.R #> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it #> # is always 15 for consistency #>  #> library(mkin)  # We use mkinmod and mkinpredict #> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) #> n <- 15 #> log_sd <- 0.3 #> err_1 = list(const = 1, prop = 0.05) #> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) #> const <- function(value) 2 #>  #> set.seed(123456) #> SFO <- mkinmod(parent = mkinsub(\"SFO\")) #> sfo_pop <- list(parent_0 = 100, k_parent = 0.03) #> sfo_parms <- as.matrix(data.frame( #>     k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd))) #> set.seed(123456) #> ds_sfo <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(SFO, sfo_parms[i, ], #>     c(parent = sfo_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_sfo, \"pop\") <- sfo_pop #> attr(ds_sfo, \"parms\") <- sfo_parms #>  #> set.seed(123456) #> FOMC <- mkinmod(parent = mkinsub(\"FOMC\")) #> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) #> fomc_parms <- as.matrix(data.frame( #>     alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), #>     beta = rlnorm(n, log(fomc_pop$beta), 0.2))) #> set.seed(123456) #> ds_fomc <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], #>     c(parent = fomc_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_fomc, \"pop\") <- fomc_pop #> attr(ds_fomc, \"parms\") <- fomc_parms #>  #> set.seed(123456) #> DFOP <- mkinmod(parent = mkinsub(\"DFOP\")) #> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) #> dfop_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(dfop_pop$k1), log_sd), #>   k2 = rlnorm(n, log(dfop_pop$k2), log_sd), #>   g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) #> set.seed(123456) #> ds_dfop <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], #>     c(parent = dfop_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_dfop, \"pop\") <- dfop_pop #> attr(ds_dfop, \"parms\") <- dfop_parms #>  #> set.seed(123456) #> HS <- mkinmod(parent = mkinsub(\"HS\")) #> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) #> hs_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(hs_pop$k1), log_sd), #>   k2 = rlnorm(n, log(hs_pop$k2), log_sd), #>   tb = rlnorm(n, log(hs_pop$tb), 0.1))) #> set.seed(123456) #> ds_hs <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(HS, hs_parms[i, ], #>     c(parent = hs_pop$parent_0), sampling_times) #>   add_err(ds_mean, const, n = 1)[[1]] #> }) #> attr(ds_hs, \"pop\") <- hs_pop #> attr(ds_hs, \"parms\") <- hs_parms #>  #> set.seed(123456) #> DFOP_SFO <- mkinmod( #>   parent = mkinsub(\"DFOP\", \"m1\"), #>   m1 = mkinsub(\"SFO\"), #>   quiet = TRUE) #> dfop_sfo_pop <- list(parent_0 = 100, #>   k_m1 = 0.007, f_parent_to_m1 = 0.5, #>   k1 = 0.1, k2 = 0.02, g = 0.5) #> dfop_sfo_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd), #>   k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd), #>   g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)), #>   f_parent_to_m1 = plogis(rnorm(n, #>       qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), #>   k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd))) #> ds_dfop_sfo_mean <- lapply(1:n, #>   function(i) { #>     mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ], #>       c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times) #>   } #> ) #> set.seed(123456) #> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) { #>   add_err(ds, #>     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), #>     n = 1, secondary = \"m1\")[[1]] #> }) #> attr(ds_dfop_sfo, \"pop\") <- dfop_sfo_pop #> attr(ds_dfop_sfo, \"parms\") <- dfop_sfo_parms #>  #> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = \"data/ds_mixed.rda\", version = 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"function calculates DT50 DT90 values well formation fractions kinetic models fitted mkinfit. SFORB model specified one parents metabolites, Eigenvalues returned. equivalent rate constants DFOP model, advantage SFORB model can also used metabolites.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"","code":"endpoints(fit, covariates = NULL, covariate_quantile = 0.5)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"fit object class mkinfit, nlme.mmkin saem.mmkin, another object list components mkinmod containing mkinmod degradation model, two numeric vectors, bparms.optim bparms.fixed, contain parameter values model. covariates Numeric vector covariate values variables covariate models object. given, overrides 'covariate_quantile'. covariate_quantile argument effect fitted object covariate models. , default show endpoints median covariate values (50th percentile).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"list matrix dissipation times named distimes, , applicable, vector formation fractions named ff , SFORB model use, vector eigenvalues SFORB models, equivalent DFOP rate constants","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"Additional DT50 values calculated FOMC DT90 k1 k2 HS DFOP, well Eigenvalues b1 b2 SFORB models","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"function used internally summary.mkinfit, summary.nlme.mmkin summary.saem.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)   endpoints(fit) #> $distimes #>            DT50    DT90 DT50back #> parent 1.785233 15.1479 4.559973 #>    # \\dontrun{     fit_2 <- mkinfit(\"DFOP\", FOCUS_2006_C, quiet = TRUE)     endpoints(fit_2) #> $distimes #>            DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent 1.886925 21.25106 6.397207 1.508293 38.83438 #>      fit_3 <- mkinfit(\"SFORB\", FOCUS_2006_C, quiet = TRUE)     endpoints(fit_3) #> $ff #> parent_free  #>           1  #>  #> $SFORB #> parent_b1 parent_b2  parent_g  #> 0.4595574 0.0178488 0.8539454  #>  #> $distimes #>            DT50     DT90 DT50back DT50_parent_b1 DT50_parent_b2 #> parent 1.886925 21.25106 6.397208       1.508293       38.83438 #>    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":null,"dir":"Reference","previous_headings":"","what":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"12 datasets extracted active substance evaluation dossiers published  EFSA. Kinetic evaluations shown datasets intended illustrate  advance error model specifications. fact data  results shown imply license use context  pesticide registrations, use data may constrained  data protection regulations. Preprocessing data performed based recommendations FOCUS  kinetics workgroup (FOCUS, 2014) described . Datasets 1 2 Renewal Assessment Report (RAR) imazamox  (France, 2015, p. 15). setting values reported zero, LOQ 0.1  assumed. Metabolite residues reported day zero added  parent compound residues. Datasets 3 4 Renewal Assessment Report (RAR) isofetamid  (Belgium, 2014, p. 8) show data two different radiolabels.  dataset 4, value given metabolite day zero sampling  replicate B added parent compound, following respective  FOCUS recommendation. Dataset 5 Renewal Assessment Report (RAR) ethofumesate  (Austria, 2015, p. 16). Datasets 6 10 Renewal Assessment Report (RAR) glyphosate  (Germany, 2013, pages 8, 28, 50, 51). initial sampling,  residues given metabolite added parent  value, following recommendation FOCUS kinetics workgroup. Dataset 11 Renewal Assessment Report (RAR) 2,4-D  (Hellas, 2013, p. 644). Values reported zero set NA,  exception day three sampling metabolite A2, set  one half LOD reported 1% AR. Dataset 12 Renewal Assessment Report (RAR) thifensulfuron-methyl  (United Kingdom, 2014, p. 81).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"","code":"experimental_data_for_UBA_2019"},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"list containing twelve datasets R6 class defined mkinds,   containing, among others, following components title name dataset, e.g. Soil 1 data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 () Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 () France (2015). Imazamox Draft Renewal Assessment Report Volume 3 Annex B.8 () FOCUS (2014) “Generic guidance Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   Version 1.1, 18 December 2014   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics Germany (2013). Renewal Assessment Report Glyphosate Volume 3 Annex B.8: Environmental Fate   Behaviour Hellas (2013). Renewal Assessment Report 2,4-D Volume 3 Annex B.8: Fate behaviour   environment Ranke (2019) Documentation results obtained error model expertise   written German Umweltbundesamt. United Kingdom (2014). Thifensulfuron-methyl - Annex B.8 (Volume 3) Report Proposed   Decision United Kingdom made European Commission Regulation (EC) .   1141/2010 renewal active substance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"","code":"# \\dontrun{  # Model definitions sfo_sfo <- mkinmod(   parent = mkinsub(\"SFO\", to = \"A1\"),   A1 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  dfop_sfo <- mkinmod(   parent = mkinsub(\"DFOP\", to = \"A1\"),   A1 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  sfo_sfo_sfo <- mkinmod(   parent = mkinsub(\"SFO\", to = \"A1\"),   A1 = mkinsub(\"SFO\", to = \"A2\"),   A2 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  dfop_sfo_sfo <- mkinmod(   parent = mkinsub(\"DFOP\", to = \"A1\"),   A1 = mkinsub(\"SFO\", to = \"A2\"),   A2 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded d_1_2 <- lapply(experimental_data_for_UBA_2019[1:2], function(x) x$data) names(d_1_2) <- paste(\"Soil\", 1:2)   f_1_2_tc <- mmkin(list(\"DFOP-SFO-SFO\" = dfop_sfo_sfo), d_1_2, error_model = \"tc\")  plot(f_1_2_tc, resplot = \"errmod\")   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"Time step normalisation factors aerobic soil degradation described Appendix 8 FOCUS kinetics guidance (FOCUS 2014, p. 369).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"","code":"f_time_norm_focus(object, ...)  # S3 method for class 'numeric' f_time_norm_focus(   object,   moisture = NA,   field_moisture = NA,   temperature = object,   Q10 = 2.58,   walker = 0.7,   f_na = NA,   ... )  # S3 method for class 'mkindsg' f_time_norm_focus(   object,   study_moisture_ref_source = c(\"auto\", \"meta\", \"focus\"),   Q10 = 2.58,   walker = 0.7,   f_na = NA,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"object object containing information used calculations ... Currently used moisture Numeric vector moisture contents \\% w/w field_moisture Numeric vector moisture contents field capacity (pF2) \\% w/w temperature Numeric vector temperatures °C Q10 Q10 value used temperature normalisation walker Walker exponent used moisture normalisation f_na factor use NA values. set NA, factors complete cases returned. study_moisture_ref_source Source reference value used calculate study moisture. 'auto', preference given reference moisture given meta information, otherwise focus soil moisture soil class used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"","code":"f_time_norm_focus(25, 20, 25) # 1.37, compare FOCUS 2014 p. 184 #> [1] 1.373956  D24_2014$meta #>                                 study usda_soil_type study_moisture_ref_type #> Mississippi                Cohen 1991      Silt loam                    <NA> #> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1 #> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1 #> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1 #> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1 #>             rel_moisture temperature #> Mississippi           NA          25 #> Fayette              0.5          20 #> RefSol 03-G          0.5          20 #> Site E1              0.5          20 #> Site I2              0.5          20 # No moisture normalisation in the first dataset, so we use f_na = 1 to get # temperature only normalisation as in the EU evaluation f_time_norm_focus(D24_2014, study_moisture_ref_source = \"focus\", f_na = 1) #> $f_time_norm was (re)set to normalised values"},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":null,"dir":"Reference","previous_headings":"","what":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"value transcribed p. 36. table assumes field capacity corresponds pF2, MWHC pF 1 1/3 bar pF 2.5.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"","code":"focus_soil_moisture"},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"matrix upper case USDA soil classes row names, water tension ('pF1', 'pF2', 'pF 2.5') column names","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"Anonymous (2014) Generic Guidance Tier 1 FOCUS Ground Water Assessment Version 2.2, May 2014 https://esdac.jrc.ec.europa.eu/projects/ground-water","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"","code":"focus_soil_moisture #>                 pF1 pF2 pF2.5 #> Sand             24  12     7 #> Loamy sand       24  14     9 #> Sandy loam       27  19    15 #> Sandy clay loam  28  22    18 #> Clay loam        32  28    25 #> Loam             31  25    21 #> Silt loam        32  26    21 #> Silty clay loam  34  30    27 #> Silt             31  27    21 #> Sandy clay       41  35    31 #> Silty clay       44  40    36 #> Clay             53  48    43"},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve a degradation function from the mmkin namespace — get_deg_func","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"Retrieve degradation function mmkin namespace","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"","code":"get_deg_func()"},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"function likely previously assigned within nlme.mmkin","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical kinetics template — hierarchical_kinetics","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"R markdown format setting hierarchical kinetics based template provided mkin package. format based rmarkdown::pdf_document. Chunk options adapted. Echoing R code code chunks caching turned per default. character prepending output code chunks set empty string, code tidying , figure alignment defaults centering, positioning figures set \"H\", means figures move around document, stay user includes .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"","code":"hierarchical_kinetics(..., keep_tex = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"... Arguments rmarkdown::pdf_document keep_tex Keep intermediate tex file used conversion PDF. Note argument control whether keep auxiliary files (e.g., .aux) generated LaTeX compiling .tex .pdf. keep files, may set options(tinytex.clean = FALSE).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"R Markdown output format pass render","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"latter feature (positioning figures \"H\") depends LaTeX package 'float'. addition, LaTeX package 'listing' used template showing model fit summaries Appendix. means LaTeX packages 'float' 'listing' need installed TeX distribution used. Windows, easiest way achieve (TeX distribution present ) install 'tinytex' R package, run 'tinytex::install_tinytex()' get basic tiny Tex distribution, run 'tinytex::tlmgr_install(c(\"float\", \"listing\"))'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"","code":"# \\dontrun{ library(rmarkdown) # The following is now commented out after the relase of v1.2.3 for the generation # of online docs, as the command creates a directory and opens an editor #draft(\"example_analysis.rmd\", template = \"hierarchical_kinetics\", package = \"mkin\") # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get the names of ill-defined parameters — illparms","title":"Method to get the names of ill-defined parameters — illparms","text":"method generalised nonlinear regression fits obtained mkinfit mmkin checks degradation parameters pass Wald test (degradation kinetics often simply called t-test) significant difference zero. test, parameterisation without parameter transformations used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get the names of ill-defined parameters — illparms","text":"","code":"illparms(object, ...)  # S3 method for class 'mkinfit' illparms(object, conf.level = 0.95, ...)  # S3 method for class 'illparms.mkinfit' print(x, ...)  # S3 method for class 'mmkin' illparms(object, conf.level = 0.95, ...)  # S3 method for class 'illparms.mmkin' print(x, ...)  # S3 method for class 'saem.mmkin' illparms(   object,   conf.level = 0.95,   random = TRUE,   errmod = TRUE,   slopes = TRUE,   ... )  # S3 method for class 'illparms.saem.mmkin' print(x, ...)  # S3 method for class 'mhmkin' illparms(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...)  # S3 method for class 'illparms.mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get the names of ill-defined parameters — illparms","text":"object object investigate ... potential future extensions conf.level confidence level checking p values x object printed random hierarchical fits, random effects tested? errmod hierarchical fits, error model parameters tested? slopes hierarchical saem fits using saemix backend, slope parameters covariate model(starting 'beta_') tested?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get the names of ill-defined parameters — illparms","text":"mkinfit saem objects, character vector parameter names. mmkin mhmkin objects, matrix like object class 'illparms.mmkin' 'illparms.mhmkin'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Method to get the names of ill-defined parameters — illparms","text":"method hierarchical model fits, also known nonlinear mixed-effects model fits obtained saem mhmkin checks confidence intervals random effects expressed standard deviations include zero, confidence intervals error model parameters include zero.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Method to get the names of ill-defined parameters — illparms","text":"return objects printing methods. single fits, printing output anything case ill-defined parameters found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Method to get the names of ill-defined parameters — illparms","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_A, quiet = TRUE) #> Warning: Optimisation did not converge: #> false convergence (8) illparms(fit) #> [1] \"parent_0\" \"alpha\"    \"beta\"     \"sigma\"    # \\dontrun{ fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS C\" = FOCUS_2006_C),   quiet = TRUE) illparms(fits) #>       dataset #> model  FOCUS A                      FOCUS C #>   SFO                                       #>   FOMC parent_0, alpha, beta, sigma         # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to perform isometric log-ratio transformation — ilr","title":"Function to perform isometric log-ratio transformation — ilr","text":"implementation special case class isometric log-ratio transformations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to perform isometric log-ratio transformation — ilr","text":"","code":"ilr(x)  invilr(x)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to perform isometric log-ratio transformation — ilr","text":"x numeric vector. Naturally, forward transformation sensible vectors elements greater zero.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to perform isometric log-ratio transformation — ilr","text":"result forward backward transformation. returned components always sum 1 case inverse log-ratio transformation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to perform isometric log-ratio transformation — ilr","text":"Peter Filzmoser, Karel Hron (2008) Outlier Detection Compositional Data Using Robust Methods. Math Geosci 40 233-248","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to perform isometric log-ratio transformation — ilr","text":"René Lehmann Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to perform isometric log-ratio transformation — ilr","text":"","code":"# Order matters ilr(c(0.1, 1, 10)) #> [1] -1.628174 -2.820079 ilr(c(10, 1, 0.1)) #> [1] 1.628174 2.820079 # Equal entries give ilr transformations with zeros as elements ilr(c(3, 3, 3)) #> [1] 0 0 # Almost equal entries give small numbers ilr(c(0.3, 0.4, 0.3)) #> [1] -0.2034219  0.1174457 # Only the ratio between the numbers counts, not their sum invilr(ilr(c(0.7, 0.29, 0.01))) #> [1] 0.70 0.29 0.01 invilr(ilr(2.1 * c(0.7, 0.29, 0.01))) #> [1] 0.70 0.29 0.01 # Inverse transformation of larger numbers gives unequal elements invilr(-10) #> [1] 7.213536e-07 9.999993e-01 invilr(c(-10, 0)) #> [1] 7.207415e-07 9.991507e-01 8.486044e-04 # The sum of the elements of the inverse ilr is 1 sum(invilr(c(-10, 0))) #> [1] 1 # This is why we do not need all elements of the inverse transformation to go back: a <- c(0.1, 0.3, 0.5) b <- invilr(a) length(b) # Four elements #> [1] 4 ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5) #> [1] 0.1 0.3 0.5"},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"Confidence intervals parameters saem.mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' intervals(object, level = 0.95, backtransform = TRUE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"object fitted saem.mmkin object level confidence level. Must default 0.95 available saemix object backtransform case model fitted mkin transformations, backtransform parameters one one correlation transformed backtransformed parameters exists? ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"object 'intervals.saem.mmkin' 'intervals.lme' class attribute","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the distribution of log likelihoods from multistart objects — llhist","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"Produces histogram log-likelihoods. addition, likelihood original fit shown red vertical line.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"","code":"llhist(object, breaks = \"Sturges\", lpos = \"topleft\", main = \"\", ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"object multistart object breaks Passed hist lpos Positioning legend. main Title plot ... Passed hist","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":null,"dir":"Reference","previous_headings":"","what":"Lack-of-fit test for models fitted to data with replicates — loftest","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"generic function method currently defined mkinfit objects. fits anova model data contained object compares likelihoods using likelihood ratio test lrtest.default lmtest package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"","code":"loftest(object, ...)  # S3 method for class 'mkinfit' loftest(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"object model object defined loftest method ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"anova model interpreted simplest form mkinfit model, assuming constant variance means, enforcing structure means, one model parameter every mean replicate samples.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"","code":"# \\dontrun{ test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == \"parent\") sfo_fit <- mkinfit(\"SFO\", test_data, quiet = TRUE) plot_res(sfo_fit) # We see a clear pattern in the residuals  loftest(sfo_fit)  # We have a clear lack of fit #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1  10 -40.710                          #> 2   3 -63.954 -7 46.487  7.027e-08 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # # We try a different model (the one that was used to generate the data) dfop_fit <- mkinfit(\"DFOP\", test_data, quiet = TRUE) plot_res(dfop_fit) # We don't see systematic deviations, but heteroscedastic residuals  # therefore we should consider adapting the error model, although we have loftest(dfop_fit) # no lack of fit #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: DFOP with error model const #>   #Df  LogLik Df Chisq Pr(>Chisq) #> 1  10 -40.710                     #> 2   5 -42.453 -5 3.485     0.6257 # # This is the anova model used internally for the comparison test_data_anova <- test_data test_data_anova$time <- as.factor(test_data_anova$time) anova_fit <- lm(value ~ time, data = test_data_anova) summary(anova_fit) #>  #> Call: #> lm(formula = value ~ time, data = test_data_anova) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -6.1000 -0.5625  0.0000  0.5625  6.1000  #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept)  103.150      2.323  44.409 7.44e-12 *** #> time1        -19.950      3.285  -6.073 0.000185 *** #> time3        -50.800      3.285 -15.465 8.65e-08 *** #> time7        -68.500      3.285 -20.854 6.28e-09 *** #> time14       -79.750      3.285 -24.278 1.63e-09 *** #> time28       -86.000      3.285 -26.181 8.35e-10 *** #> time60       -94.900      3.285 -28.891 3.48e-10 *** #> time90       -98.500      3.285 -29.986 2.49e-10 *** #> time120     -100.450      3.285 -30.580 2.09e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #> Residual standard error: 3.285 on 9 degrees of freedom #> Multiple R-squared:  0.9953,\tAdjusted R-squared:  0.9912  #> F-statistic: 240.5 on 8 and 9 DF,  p-value: 1.417e-09 #>  logLik(anova_fit) # We get the same likelihood and degrees of freedom #> 'log Lik.' -40.71015 (df=10) # test_data_2 <- synthetic_data_for_UBA_2014[[12]]$data m_synth_SFO_lin <- mkinmod(parent = list(type = \"SFO\", to = \"M1\"),   M1 = list(type = \"SFO\", to = \"M2\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded sfo_lin_fit <- mkinfit(m_synth_SFO_lin, test_data_2, quiet = TRUE) plot_res(sfo_lin_fit) # not a good model, we try parallel formation  loftest(sfo_lin_fit) #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq)     #> 1  28  -93.606                           #> 2   7 -171.927 -21 156.64  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # m_synth_SFO_par <- mkinmod(parent = list(type = \"SFO\", to = c(\"M1\", \"M2\")),   M1 = list(type = \"SFO\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded sfo_par_fit <- mkinfit(m_synth_SFO_par, test_data_2, quiet = TRUE) plot_res(sfo_par_fit) # much better for metabolites  loftest(sfo_par_fit) #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq)     #> 1  28  -93.606                           #> 2   7 -156.331 -21 125.45  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # m_synth_DFOP_par <- mkinmod(parent = list(type = \"DFOP\", to = c(\"M1\", \"M2\")),   M1 = list(type = \"SFO\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded dfop_par_fit <- mkinfit(m_synth_DFOP_par, test_data_2, quiet = TRUE) plot_res(dfop_par_fit) # No visual lack of fit  loftest(dfop_par_fit)  # no lack of fit found by the test #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq) #> 1  28  -93.606                       #> 2   9 -102.763 -19 18.313     0.5016 # # The anova model used for comparison in the case of transformation products test_data_anova_2 <- dfop_par_fit$data test_data_anova_2$variable <- as.factor(test_data_anova_2$variable) test_data_anova_2$time <- as.factor(test_data_anova_2$time) anova_fit_2 <- lm(observed ~ time:variable - 1, data = test_data_anova_2) summary(anova_fit_2) #>  #> Call: #> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -6.1000 -0.5875  0.0000  0.5875  6.1000  #>  #> Coefficients: (2 not defined because of singularities) #>                        Estimate Std. Error t value Pr(>|t|)     #> time0:variableparent    103.150      1.573  65.562  < 2e-16 *** #> time1:variableparent     83.200      1.573  52.882  < 2e-16 *** #> time3:variableparent     52.350      1.573  33.274  < 2e-16 *** #> time7:variableparent     34.650      1.573  22.024  < 2e-16 *** #> time14:variableparent    23.400      1.573  14.873 6.35e-14 *** #> time28:variableparent    17.150      1.573  10.901 5.47e-11 *** #> time60:variableparent     8.250      1.573   5.244 1.99e-05 *** #> time90:variableparent     4.650      1.573   2.956 0.006717 **  #> time120:variableparent    2.700      1.573   1.716 0.098507 .   #> time0:variableM1             NA         NA      NA       NA     #> time1:variableM1         11.850      1.573   7.532 6.93e-08 *** #> time3:variableM1         22.700      1.573  14.428 1.26e-13 *** #> time7:variableM1         33.050      1.573  21.007  < 2e-16 *** #> time14:variableM1        31.250      1.573  19.863  < 2e-16 *** #> time28:variableM1        18.900      1.573  12.013 7.02e-12 *** #> time60:variableM1         7.550      1.573   4.799 6.28e-05 *** #> time90:variableM1         3.850      1.573   2.447 0.021772 *   #> time120:variableM1        2.050      1.573   1.303 0.204454     #> time0:variableM2             NA         NA      NA       NA     #> time1:variableM2          6.700      1.573   4.259 0.000254 *** #> time3:variableM2         16.750      1.573  10.646 8.93e-11 *** #> time7:variableM2         25.800      1.573  16.399 6.89e-15 *** #> time14:variableM2        28.600      1.573  18.178 6.35e-16 *** #> time28:variableM2        25.400      1.573  16.144 9.85e-15 *** #> time60:variableM2        21.600      1.573  13.729 3.81e-13 *** #> time90:variableM2        17.800      1.573  11.314 2.51e-11 *** #> time120:variableM2       14.100      1.573   8.962 2.79e-09 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #> Residual standard error: 2.225 on 25 degrees of freedom #> Multiple R-squared:  0.9979,\tAdjusted R-squared:  0.9957  #> F-statistic: 469.2 on 25 and 25 DF,  p-value: < 2.2e-16 #>  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"function returns product likelihood densities observed value, calculated part fitting procedure using dnorm, .e. assuming normal distribution, means predicted degradation model, standard deviations predicted error model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"","code":"# S3 method for class 'mkinfit' logLik(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"object object class mkinfit. ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"object class logLik number estimated parameters (degradation model parameters plus variance model parameters) attribute.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"total number estimated parameters returned value likelihood calculated sum fitted degradation model parameters fitted error model parameters.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"","code":"# \\dontrun{   sfo_sfo <- mkinmod(     parent = mkinsub(\"SFO\", to = \"m1\"),     m1 = mkinsub(\"SFO\")   ) #> Temporary DLL for differentials generated and loaded   d_t <- subset(FOCUS_2006_D, value != 0)   f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)   f_obs <- update(f_nw, error_model = \"obs\")   f_tc <- update(f_nw, error_model = \"tc\")   AIC(f_nw, f_obs, f_tc) #>       df      AIC #> f_nw   5 204.4486 #> f_obs  6 205.8727 #> f_tc   6 141.9656   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"logLik method for saem.mmkin objects — logLik.saem.mmkin","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"logLik method saem.mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' logLik(object, ..., method = c(\"is\", \"lin\", \"gq\"))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"object fitted saem.mmkin object ... Passed saemix::logLik.SaemixObject method Passed saemix::logLik.SaemixObject","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic kinetics — logistic.solution","title":"Logistic kinetics — logistic.solution","text":"Function describing exponential decline defined starting value, increasing rate constant, supposedly caused microbial growth","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic kinetics — logistic.solution","text":"","code":"logistic.solution(t, parent_0, kmax, k0, r)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic kinetics — logistic.solution","text":"t Time. parent_0 Starting value response variable time zero. kmax Maximum rate constant. k0 Minimum rate constant effective time zero. r Growth rate increase rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic kinetics — logistic.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logistic kinetics — logistic.solution","text":"solution logistic model reduces SFO.solution k0 equal kmax.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Logistic kinetics — logistic.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logistic kinetics — logistic.solution","text":"","code":"# Reproduce the plot on page 57 of FOCUS (2014)   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),        from = 0, to = 100, ylim = c(0, 100),        xlab = \"Time\", ylab = \"Residue\")   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),        from = 0, to = 100, add = TRUE, lty = 2, col = 2)   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),        from = 0, to = 100, add = TRUE, lty = 3, col = 3)   plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),        from = 0, to = 100, add = TRUE, lty = 4, col = 4)   plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),        from = 0, to = 100, add = TRUE, lty = 5, col = 5)   legend(\"topright\", inset = 0.05,          legend = paste0(\"k0 = \", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),                          \", r = \", c(0.2, 0.4, 0.8, 0.2, 0.2)),          lty = 1:5, col = 1:5)     # Fit with synthetic data   logistic <- mkinmod(parent = mkinsub(\"logistic\"))    sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)   parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2)   d_logistic <- mkinpredict(logistic,     parms_logistic, c(parent = 100),     sampling_times)   d_2_1 <- add_err(d_logistic,     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),     n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]    m <- mkinfit(\"logistic\", d_2_1, quiet = TRUE)   plot_sep(m)    summary(m)$bpar #>              Estimate   se_notrans   t value       Pr(>t)        Lower #> parent_0 1.057896e+02 1.9023449604 55.610120 3.768360e-16 1.016451e+02 #> kmax     6.398190e-02 0.0143201030  4.467978 3.841828e-04 3.929235e-02 #> k0       1.612775e-04 0.0005866813  0.274898 3.940351e-01 5.846688e-08 #> r        2.263946e-01 0.1718110664  1.317695 1.061043e-01 4.335843e-02 #> sigma    5.332935e+00 0.9145907310  5.830952 4.036926e-05 3.340213e+00 #>                Upper #> parent_0 109.9341588 #> kmax       0.1041853 #> k0         0.4448749 #> r          1.1821120 #> sigma      7.3256566   endpoints(m)$distimes #>            DT50     DT90  DT50_k0 DT50_kmax #> parent 36.86533 62.41511 4297.853  10.83349"},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"Compare two mkinfit models based likelihood. two fitted mkinfit objects given arguments, checked fitted data. responsibility user make sure models nested, .e. one less degrees freedom can expressed fixing parameters .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"","code":"# S3 method for class 'mkinfit' lrtest(object, object_2 = NULL, ...)  # S3 method for class 'mmkin' lrtest(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"object mkinfit object, mmkin column object containing two fits data. object_2 Optionally, another mkinfit object fitted data. ... Argument mkinfit, passed update.mkinfit creating alternative fitted object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"Alternatively, argument mkinfit can given passed update.mkinfit obtain alternative model. comparison made lrtest.default method lmtest package. model higher number fitted parameters (alternative hypothesis) listed first, model lower number fitted parameters (null hypothesis).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"","code":"# \\dontrun{ test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == \"parent\") sfo_fit <- mkinfit(\"SFO\", test_data, quiet = TRUE) dfop_fit <- mkinfit(\"DFOP\", test_data, quiet = TRUE) lrtest(dfop_fit, sfo_fit) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   3 -63.954 -2 43.002  4.594e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 lrtest(sfo_fit, dfop_fit) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   3 -63.954 -2 43.002  4.594e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  # The following two examples are commented out as they fail during # generation of the static help pages by pkgdown #lrtest(dfop_fit, error_model = \"tc\") #lrtest(dfop_fit, fixed_parms = c(k2 = 0))  # However, this equivalent syntax also works for static help pages lrtest(dfop_fit, update(dfop_fit, error_model = \"tc\")) #> Likelihood ratio test #>  #> Model 1: DFOP with error model tc #> Model 2: DFOP with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -34.587                          #> 2   5 -42.453 -1 15.731  7.302e-05 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 lrtest(dfop_fit, update(dfop_fit, fixed_parms = c(k2 = 0))) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: DFOP with error model const and fixed parameter(s) k2 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   4 -57.340 -1 29.776  4.851e-08 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"function calculates maximum moving window time weighted average concentrations (TWAs) kinetic models fitted mkinfit. Currently, calculations parent implemented SFO, FOMC, DFOP HS models, using analytical formulas given PEC soil section FOCUS guidance.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"","code":"max_twa_parent(fit, windows)  max_twa_sfo(M0 = 1, k, t)  max_twa_fomc(M0 = 1, alpha, beta, t)  max_twa_dfop(M0 = 1, k1, k2, g, t)  max_twa_hs(M0 = 1, k1, k2, tb, t)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"fit object class mkinfit. windows width time windows TWAs calculated. M0 initial concentration maximum time weighted average decline curve calculated. default use value 1, means relative maximum time weighted average factor (f_twa) calculated. k rate constant case SFO kinetics. t width time window. alpha Parameter FOMC model. beta Parameter FOMC model. k1 first rate constant DFOP HS kinetics. k2 second rate constant DFOP HS kinetics. g Parameter DFOP model. tb Parameter HS model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"max_twa_parent, numeric vector, named using windows argument.  functions, numeric vector length one (also known 'number').","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)   max_twa_parent(fit, c(7, 21)) #>        7       21  #> 34.71343 18.22124"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"Time course 2,4,5-trichlorophenoxyacetic acid, corresponding  2,4,5-trichlorophenol 2,4,5-trichloroanisole recovered diethylether  extracts.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"","code":"mccall81_245T"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"dataframe containing following variables. name name compound observed. Note T245 used                        acronym 2,4,5-T. T245 legitimate object name                        R, necessary specifying models using                        mkinmod. time numeric vector containing sampling times days                        treatment value numeric vector containing concentrations percent applied radioactivity soil factor containing name soil","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"McCall P, Vrona SA, Kelley SS (1981) Fate uniformly carbon-14 ring labelled 2,4,5-Trichlorophenoxyacetic acid 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107   doi:10.1021/jf00103a026","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"","code":"SFO_SFO_SFO <- mkinmod(T245 = list(type = \"SFO\", to = \"phenol\"),     phenol = list(type = \"SFO\", to = \"anisole\"),     anisole = list(type = \"SFO\")) #> Temporary DLL for differentials generated and loaded   # \\dontrun{     fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == \"Commerce\"), quiet = TRUE) #> Warning: Observations with value of zero were removed from the data     summary(fit.1)$bpar #>                         Estimate   se_notrans   t value       Pr(>t) #> T245_0              1.038550e+02 2.1847074943 47.537272 4.472189e-18 #> k_T245              4.337042e-02 0.0018983965 22.845818 2.276911e-13 #> k_phenol            4.050581e-01 0.2986993738  1.356073 9.756990e-02 #> k_anisole           6.678742e-03 0.0008021439  8.326114 2.623177e-07 #> f_T245_to_phenol    6.227599e-01 0.3985340721  1.562626 6.949414e-02 #> f_phenol_to_anisole 1.000000e+00 0.6718440131  1.488441 7.867790e-02 #> sigma               2.514628e+00 0.4907558973  5.123989 6.233159e-05 #>                            Lower        Upper #> T245_0              99.246061490 1.084640e+02 #> k_T245               0.039631621 4.746194e-02 #> k_phenol             0.218013879 7.525762e-01 #> k_anisole            0.005370739 8.305299e-03 #> f_T245_to_phenol     0.547559080 6.924813e-01 #> f_phenol_to_anisole  0.000000000 1.000000e+00 #> sigma                1.706607296 3.322649e+00     endpoints(fit.1) #> $ff #>    T245_phenol      T245_sink phenol_anisole    phenol_sink  #>   6.227599e-01   3.772401e-01   1.000000e+00   3.072478e-10  #>  #> $distimes #>               DT50      DT90 #> T245     15.982025  53.09114 #> phenol    1.711229   5.68458 #> anisole 103.784093 344.76329 #>      # formation fraction from phenol to anisol is practically 1. As we cannot     # fix formation fractions when using the ilr transformation, we can turn of     # the sink in the model generation     SFO_SFO_SFO_2 <- mkinmod(T245 = list(type = \"SFO\", to = \"phenol\"),       phenol = list(type = \"SFO\", to = \"anisole\", sink = FALSE),       anisole = list(type = \"SFO\")) #> Temporary DLL for differentials generated and loaded     fit.2 <- mkinfit(SFO_SFO_SFO_2, subset(mccall81_245T, soil == \"Commerce\"),       quiet = TRUE) #> Warning: Observations with value of zero were removed from the data     summary(fit.2)$bpar #>                      Estimate   se_notrans   t value       Pr(>t)        Lower #> T245_0           1.038550e+02 2.1623653059 48.028439 4.993108e-19 99.271020328 #> k_T245           4.337042e-02 0.0018343666 23.643268 3.573556e-14  0.039650976 #> k_phenol         4.050582e-01 0.1177237651  3.440752 1.679255e-03  0.218746589 #> k_anisole        6.678742e-03 0.0006829745  9.778903 1.872894e-08  0.005377083 #> f_T245_to_phenol 6.227599e-01 0.0342197873 18.198824 2.039411e-12  0.547975634 #> sigma            2.514628e+00 0.3790944250  6.633250 2.875782e-06  1.710983655 #>                         Upper #> T245_0           108.43904079 #> k_T245             0.04743877 #> k_phenol           0.75005593 #> k_anisole          0.00829550 #> f_T245_to_phenol   0.69212307 #> sigma              3.31827222     endpoints(fit.1) #> $ff #>    T245_phenol      T245_sink phenol_anisole    phenol_sink  #>   6.227599e-01   3.772401e-01   1.000000e+00   3.072478e-10  #>  #> $distimes #>               DT50      DT90 #> T245     15.982025  53.09114 #> phenol    1.711229   5.68458 #> anisole 103.784093 344.76329 #>      plot_sep(fit.2)    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"Calculate mean degradation parameters mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"","code":"mean_degparms(   object,   random = FALSE,   test_log_parms = FALSE,   conf.level = 0.6,   default_log_parms = NA )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"object mmkin row object containing several fits model different datasets random list fixed random effects returned? test_log_parms TRUE, log parameters considered mean calculations untransformed counterparts (likely rate constants) pass t-test significant difference zero. conf.level Possibility adjust required confidence level parameter tested requested 'test_log_parms'. default_log_parms set numeric value, used default value tested log parameters failed t-test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"random FALSE (default), named vector containing mean values fitted degradation model parameters. random TRUE, list fixed random effects, format required start argument nlme case single grouping variable ds.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"name methods expresses (multiple) hierarchichal (also known multilevel) multicompartment kinetic models fitted. kinetic models nonlinear, can use various nonlinear mixed-effects model fitting functions.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"","code":"mhmkin(objects, ...)  # S3 method for class 'mmkin' mhmkin(objects, ...)  # S3 method for class 'list' mhmkin(   objects,   backend = \"saemix\",   algorithm = \"saem\",   no_random_effect = NULL,   ...,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL )  # S3 method for class 'mhmkin' x[i, j, ..., drop = FALSE]  # S3 method for class 'mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"objects list mmkin objects containing fits degradation models data, using different error models. Alternatively, single mmkin object containing fits several degradation models data ... arguments passed nonlinear mixed-effects model fitting function. backend backend used fitting. Currently, saemix supported algorithm algorithm used fitting (currently used) no_random_effect Default NULL passed saem. character vector supplied, passed calls saem, exclude random effects matching parameters. Alternatively, list character vectors object class illparms.mhmkin can specified. dimensions return object current call , .e. number rows must match number degradation models mmkin object(s), number columns must match number error models used mmkin object(s). cores number cores used multicore processing. used cluster argument NULL. Windows machines, cores > 1 supported, need use cluster argument use multiple logical processors. Per default, cores detected parallel::detectCores() used, except Windows default 1. cluster cluster returned makeCluster used parallel execution. x mhmkin object. Row index selecting fits specific models j Column index selecting fits specific datasets drop FALSE, method always returns mhmkin object, otherwise either list fit objects single fit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"two-dimensional array fit objects /try-errors can indexed using degradation model names first index (row index) error model names second index (column index), class attribute 'mhmkin'. object inheriting mhmkin.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"","code":"# \\dontrun{ # We start with separate evaluations of all the first six datasets with two # degradation models and two error models f_sep_const <- mmkin(c(\"SFO\", \"FOMC\"), ds_fomc[1:6], cores = 2, quiet = TRUE) f_sep_tc <- update(f_sep_const, error_model = \"tc\") # The mhmkin function sets up hierarchical degradation models aka # nonlinear mixed-effects models for all four combinations, specifying # uncorrelated random effects for all degradation parameters f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) status(f_saem_1) #>            error #> degradation const tc #>        SFO  OK    OK #>        FOMC OK    OK #>  #> OK: Fit terminated successfully # The 'illparms' function shows that in all hierarchical fits, at least # one random effect is ill-defined (the confidence interval for the # random effect expressed as standard deviation includes zero) illparms(f_saem_1) #>            error #> degradation const        tc                         #>        SFO  sd(parent_0) sd(parent_0)               #>        FOMC sd(log_beta) sd(parent_0), sd(log_beta) # Therefore we repeat the fits, excluding the ill-defined random effects f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) #>            error #> degradation const tc #>        SFO  OK    OK #>        FOMC OK    OK #>  #> OK: Fit terminated successfully illparms(f_saem_2) #>            error #> degradation const tc #>        SFO           #>        FOMC          # Model comparisons show that FOMC with two-component error is preferable, # and confirms our reduction of the default parameter model anova(f_saem_1) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     5 574.40 573.35 -282.20 #> SFO tc        6 543.72 542.47 -265.86 #> FOMC const    7 489.67 488.22 -237.84 #> FOMC tc       8 406.11 404.44 -195.05 anova(f_saem_2) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     4 572.22 571.39 -282.11 #> SFO tc        5 541.63 540.59 -265.81 #> FOMC const    6 487.38 486.13 -237.69 #> FOMC tc       6 402.12 400.88 -195.06 # The convergence plot for the selected model looks fine saemix::plot(f_saem_2[[\"FOMC\", \"tc\"]]$so, plot.type = \"convergence\")  # The plot of predictions versus data shows that we have a pretty data-rich # situation with homogeneous distribution of residuals, because we used the # same degradation model, error model and parameter distribution model that # was used in the data generation. plot(f_saem_2[[\"FOMC\", \"tc\"]])  # We can specify the same parameter model reductions manually no_ranef <- list(\"parent_0\", \"log_beta\", \"parent_0\", c(\"parent_0\", \"log_beta\")) dim(no_ranef) <- c(2, 2) f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) anova(f_saem_2m) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     4 572.22 571.39 -282.11 #> SFO tc        5 541.63 540.59 -265.81 #> FOMC const    6 487.38 486.13 -237.69 #> FOMC tc       6 402.12 400.88 -195.06 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a mixed effects model from an mmkin row object — mixed","title":"Create a mixed effects model from an mmkin row object — mixed","text":"Create mixed effects model mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a mixed effects model from an mmkin row object — mixed","text":"","code":"mixed(object, ...)  # S3 method for class 'mmkin' mixed(object, method = c(\"none\"), ...)  # S3 method for class 'mixed.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a mixed effects model from an mmkin row object — mixed","text":"object mmkin row object ... Currently used method method used x mixed.mmkin object print digits Number digits use printing.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a mixed effects model from an mmkin row object — mixed","text":"object class 'mixed.mmkin' observed data single dataframe convenient plotting","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a mixed effects model from an mmkin row object — mixed","text":"","code":"sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) n_biphasic <- 8 err_1 = list(const = 1, prop = 0.07)  DFOP_SFO <- mkinmod(   parent = mkinsub(\"DFOP\", \"m1\"),   m1 = mkinsub(\"SFO\"),   quiet = TRUE)  set.seed(123456) log_sd <- 0.3 syn_biphasic_parms <- as.matrix(data.frame(   k1 = rlnorm(n_biphasic, log(0.05), log_sd),   k2 = rlnorm(n_biphasic, log(0.01), log_sd),   g = plogis(rnorm(n_biphasic, 0, log_sd)),   f_parent_to_m1 = plogis(rnorm(n_biphasic, 0, log_sd)),   k_m1 = rlnorm(n_biphasic, log(0.002), log_sd)))  ds_biphasic_mean <- lapply(1:n_biphasic,   function(i) {     mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ],       c(parent = 100, m1 = 0), sampling_times)   } )  set.seed(123456L) ds_biphasic <- lapply(ds_biphasic_mean, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),     n = 1, secondary = \"m1\")[[1]] })  # \\dontrun{ f_mmkin <- mmkin(list(\"DFOP-SFO\" = DFOP_SFO), ds_biphasic, error_model = \"tc\", quiet = TRUE)  f_mixed <- mixed(f_mmkin) print(f_mixed) #> Kinetic model fitted by nonlinear regression to each dataset #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 271 observations of 2 variable(s) grouped in 8 datasets #>  #> <mmkin> object #> Status of individual fits: #>  #>           dataset #> model      1  2  3  4  5  6 7  8  #>   DFOP-SFO OK OK OK OK OK C OK OK #>  #> C: Optimisation did not converge: #> iteration limit reached without convergence (10) #> OK: No warnings #>  #> Mean fitted parameters: #>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2  #>      100.605312       -8.758664       -0.001917       -3.350887       -3.990017  #>        g_qlogis  #>       -0.091167  plot(f_mixed)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a dataframe from long to wide format — mkin_long_to_wide","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"function takes dataframe long form, .e. row observed value, converts dataframe one independent variable several dependent variables columns.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"","code":"mkin_long_to_wide(long_data, time = \"time\", outtime = \"time\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"long_data dataframe must contain one variable called \"time\" time values specified time argument, one column called \"name\" grouping observed values, finally one column observed values called \"value\". time name time variable long input data. outtime name time variable wide output data.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"Dataframe wide format.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"","code":"mkin_long_to_wide(FOCUS_2006_D) #>    time parent    m1 #> 1     0  99.46  0.00 #> 2     0 102.04  0.00 #> 3     1  93.50  4.84 #> 4     1  92.50  5.64 #> 5     3  63.23 12.91 #> 6     3  68.99 12.96 #> 7     7  52.32 22.97 #> 8     7  55.13 24.47 #> 9    14  27.27 41.69 #> 10   14  26.64 33.21 #> 11   21  11.50 44.37 #> 12   21  11.64 46.44 #> 13   35   2.85 41.22 #> 14   35   2.91 37.95 #> 15   50   0.69 41.19 #> 16   50   0.63 40.01 #> 17   75   0.05 40.09 #> 18   75   0.06 33.85 #> 19  100     NA 31.04 #> 20  100     NA 33.13 #> 21  120     NA 25.15 #> 22  120     NA 33.31"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"function simply takes dataframe one independent variable several dependent variable converts long form required mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"","code":"mkin_wide_to_long(wide_data, time = \"t\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"wide_data dataframe must contain one variable time values specified time argument usually one column observed values. time name time variable.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"Dataframe long format needed mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"","code":"wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5)) mkin_wide_to_long(wide) #>   name time value #> 1    x    1     1 #> 2    x    2     4 #> 3    x    3     7 #> 4    y    1     3 #> 5    y    2     4 #> 6    y    3     5"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":null,"dir":"Reference","previous_headings":"","what":"A dataset class for mkin — mkinds","title":"A dataset class for mkin — mkinds","text":"moment dataset class hardly used mkin. example, mkinfit take mkinds datasets argument, works dataframes one contained data field mkinds objects. datasets provided package come mkinds objects nevertheless.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"# S3 method for class 'mkinds' print(x, data = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"x mkinds object. data data printed? ... used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"A dataset class for mkin — mkinds","text":"title full title dataset sampling_times sampling times time_unit time unit observed Names observed variables unit unit observations replicates maximum number replicates per sampling time data data frame least columns name, time value order compatible mkinfit","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"A dataset class for mkin — mkinds","text":"mkinds$new() mkinds$clone()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"A dataset class for mkin — mkinds","text":"Create new mkinds object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"mkinds$new(title = \"\", data, time_unit = NA, unit = NA)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"title dataset title data data time_unit time unit unit unit observations","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"A dataset class for mkin — mkinds","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"mkinds$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A dataset class for mkin — mkinds","text":"","code":"mds <- mkinds$new(\"FOCUS A\", FOCUS_2006_A) print(mds) #> <mkinds> with $title:  FOCUS A  #> Observed compounds $observed:  parent  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 62, 90, 118  #> With a maximum of  1  replicates"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":null,"dir":"Reference","previous_headings":"","what":"A class for dataset groups for mkin — mkindsg","title":"A class for dataset groups for mkin — mkindsg","text":"container working datasets share least one compound, combined evaluations desirable. Time normalisation factors initialised value 1 dataset data supplied.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"# S3 method for class 'mkindsg' print(x, data = FALSE, verbose = data, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"x mkindsg object. data mkinds objects printed data? verbose mkinds objects printed? ... used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"A class for dataset groups for mkin — mkindsg","text":"Currently, functions making use defined class structure available package. Refer D24_2014 example dataset structure, example evaluations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"A class for dataset groups for mkin — mkindsg","text":"title title dataset group ds list mkinds objects observed_n Occurrence counts compounds datasets f_time_norm Time normalisation factors meta data frame row dataset, containing additional information form categorical data (factors) numerical data (e.g. temperature, moisture, covariates like soil pH).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"A class for dataset groups for mkin — mkindsg","text":"mkindsg$new() mkindsg$clone()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"A class for dataset groups for mkin — mkindsg","text":"Create new mkindsg object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mkindsg$new(title = \"\", ds, f_time_norm = rep(1, length(ds)), meta)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"title title ds list mkinds objects f_time_norm Time normalisation factors meta meta data","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"A class for dataset groups for mkin — mkindsg","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mkindsg$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mdsg <- mkindsg$new(\"Experimental X\", experimental_data_for_UBA_2019[6:10]) print(mdsg) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  print(mdsg, verbose = TRUE) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  #>  #> Datasets $ds: #> <mkinds> with $title:  Soil 6  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 7  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 60, 90, 120, 180  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 8  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 9  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 10  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 8, 14, 21, 41, 63, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  print(mdsg, verbose = TRUE, data = TRUE) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  #>  #> Datasets $ds: #> <mkinds> with $title:  Soil 6  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   97.2   NA #> 2     0   96.4   NA #> 3     3   71.1  4.3 #> 4     3   69.2  4.6 #> 5     6   58.1  7.0 #> 6     6   56.6  7.2 #> 7    10   44.4  8.2 #> 8    10   43.4  8.0 #> 9    20   33.3 11.0 #> 10   20   29.2 13.7 #> 11   34   17.6 11.5 #> 12   34   18.0 12.7 #> 13   55   10.5 14.9 #> 14   55    9.3 14.5 #> 15   90    4.5 12.1 #> 16   90    4.7 12.3 #> 17  112    3.0  9.9 #> 18  112    3.4 10.2 #> 19  132    2.3  8.8 #> 20  132    2.7  7.8 #>  #> <mkinds> with $title:  Soil 7  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 60, 90, 120, 180  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   93.6   NA #> 2     0   92.3   NA #> 3     3   87.0  3.9 #> 4     3   82.2  3.1 #> 5     7   74.0  6.9 #> 6     7   73.9  6.6 #> 7    14   64.2 10.4 #> 8    14   69.5  8.3 #> 9    30   54.0 14.4 #> 10   30   54.6 13.7 #> 11   60   41.1 22.1 #> 12   60   38.4 22.3 #> 13   90   32.5 27.5 #> 14   90   35.5 25.4 #> 15  120   28.1 28.0 #> 16  120   29.0 26.6 #> 17  180   26.5 25.8 #> 18  180   27.6 25.3 #>  #> <mkinds> with $title:  Soil 8  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   91.9   NA #> 2     0   90.8   NA #> 3     1   64.9  9.6 #> 4     1   66.2  7.7 #> 5     3   43.5 15.0 #> 6     3   44.1 15.1 #> 7     8   18.3 21.2 #> 8     8   18.1 21.1 #> 9    14   10.2 19.7 #> 10   14   10.8 18.9 #> 11   27    4.9 17.5 #> 12   27    3.3 15.9 #> 13   48    1.6  9.5 #> 14   48    1.5  9.8 #> 15   70    1.1  6.2 #> 16   70    0.9  6.1 #>  #> <mkinds> with $title:  Soil 9  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   99.8   NA #> 2     0   98.3   NA #> 3     1   77.1  4.2 #> 4     1   77.2  3.9 #> 5     3   59.0  7.4 #> 6     3   58.1  7.9 #> 7     8   27.4 14.5 #> 8     8   29.2 13.7 #> 9    14   19.1 14.2 #> 10   14   29.6 12.2 #> 11   27   10.1 13.7 #> 12   27   18.2 13.2 #> 13   48    4.5 13.6 #> 14   48    9.1 15.4 #> 15   70    2.3 10.4 #> 16   70    2.9 11.6 #> 17   91    2.0 10.0 #> 18   91    1.8  9.5 #> 19  120    2.0  9.1 #> 20  120    2.2  9.0 #>  #> <mkinds> with $title:  Soil 10  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 8, 14, 21, 41, 63, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   96.1   NA #> 2     0   94.3   NA #> 3     8   73.9  3.3 #> 4     8   73.9  3.4 #> 5    14   69.4  3.9 #> 6    14   73.1  2.9 #> 7    21   65.6  6.4 #> 8    21   65.3  7.2 #> 9    41   55.9  9.1 #> 10   41   54.4  8.5 #> 11   63   47.0 11.7 #> 12   63   49.3 12.0 #> 13   91   44.7 13.3 #> 14   91   46.7 13.2 #> 15  120   42.1 14.3 #> 16  120   41.3 12.1"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"function finds smallest relative error still resulting passing chi-squared test defined FOCUS kinetics report 2006.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"","code":"mkinerrmin(fit, alpha = 0.05)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"fit object class mkinfit. alpha confidence level chosen chi-squared test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"dataframe following components: err.min relative error, expressed fraction. n.optim number optimised parameters attributed data series. df number remaining degrees freedom chi2 error level calculations.  Note mean values used chi2 statistic therefore every time point observed values series counts one time. dataframe one row total dataset one row observed state variable model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"function used internally summary.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"","code":"SFO_SFO = mkinmod(parent = mkinsub(\"SFO\", to = \"m1\"),                   m1 = mkinsub(\"SFO\"),                   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data round(mkinerrmin(fit_FOCUS_D), 4) #>          err.min n.optim df #> All data  0.0640       4 15 #> parent    0.0646       2  7 #> m1        0.0469       2  8 # \\dontrun{   fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)   round(mkinerrmin(fit_FOCUS_E), 4) #>          err.min n.optim df #> All data  0.1544       4 13 #> parent    0.1659       2  7 #> m1        0.1095       2  6 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"function plots squared residuals specified subset observed variables mkinfit object. addition, one dashed line(s) show fitted error model.  combined plot fitted model error model plot can obtained plot.mkinfit using argument show_errplot = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"","code":"mkinerrplot(   object,   obs_vars = names(object$mkinmod$map),   xlim = c(0, 1.1 * max(object$data$predicted)),   xlab = \"Predicted\",   ylab = \"Squared residual\",   maxy = \"auto\",   legend = TRUE,   lpos = \"topright\",   col_obs = \"auto\",   pch_obs = \"auto\",   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"object fit represented mkinfit object. obs_vars character vector names observed variables residuals plotted. Defaults observed variables model xlim plot range x direction. xlab Label x axis. ylab Label y axis. maxy Maximum value residuals. used scaling y axis defaults \"auto\". legend legend plotted? lpos legend placed? Default \"topright\". passed legend. col_obs Colors observed variables. pch_obs Symbols used observed variables. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"","code":"# \\dontrun{ model <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, FOCUS_2006_D, error_model = \"tc\", quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinerrplot(fit)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a kinetic model to data with one or more state variables — mkinfit","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"function maximises likelihood observed data using Port algorithm stats::nlminb(), specified initial fixed parameters starting values.  step optimisation, kinetic model solved using function mkinpredict(), except analytical solution implemented, case model solved using degradation function mkinmod object. parameters selected error model fitted simultaneously degradation model parameters, arguments likelihood function.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"","code":"mkinfit(   mkinmod,   observed,   parms.ini = \"auto\",   state.ini = \"auto\",   err.ini = \"auto\",   fixed_parms = NULL,   fixed_initials = names(mkinmod$diffs)[-1],   from_max_mean = FALSE,   solution_type = c(\"auto\", \"analytical\", \"eigen\", \"deSolve\"),   method.ode = \"lsoda\",   use_compiled = \"auto\",   control = list(eval.max = 300, iter.max = 200),   transform_rates = TRUE,   transform_fractions = TRUE,   quiet = FALSE,   atol = 1e-08,   rtol = 1e-10,   error_model = c(\"const\", \"obs\", \"tc\"),   error_model_algorithm = c(\"auto\", \"d_3\", \"direct\", \"twostep\", \"threestep\", \"fourstep\",     \"IRLS\", \"OLS\"),   reweight.tol = 1e-08,   reweight.max.iter = 10,   trace_parms = FALSE,   test_residuals = FALSE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"mkinmod list class mkinmod, containing kinetic model fitted data, one shorthand names (\"SFO\", \"FOMC\", \"DFOP\", \"HS\", \"SFORB\", \"IORE\"). shorthand name given, parent degradation model generated variable highest value observed. observed dataframe object coercible dataframe (e.g. tibble) observed data.  first column called \"name\" must contain name observed variable data point. second column must contain times observation, named \"time\". third column must named \"value\" contain observed values. Zero values \"value\" column removed, warning, order avoid problems fitting two-component error model. expected problem, general, values zero observed degradation data, lower limit detection. parms.ini named vector initial values parameters, including parameters optimised potentially also fixed parameters indicated fixed_parms.  set \"auto\", initial values rate constants set default values.  Using parameter names model gives error. possible specify subset parameters model needs. can use parameter lists \"bparms.ode\" previously fitted model, contains differential equation parameters model.  works nicely models nested. example given . state.ini named vector initial values state variables model. case observed variables represented one model variable, names differ names observed variables (see map component mkinmod). default set initial value first model variable mean time zero values variable maximum observed value, others 0.  variable time zero observations, initial value set 100. err.ini named vector initial values error model parameters optimised.  set \"auto\", initial values set default values.  Otherwise, inital values error model parameters must given. fixed_parms names parameters optimised rather kept values specified parms.ini. Alternatively, named numeric vector parameters fixed, regardless values parms.ini. fixed_initials names model variables initial state time 0 excluded optimisation. Defaults state variables except first one. from_max_mean set TRUE, model one observed variable, data time maximum observed value (averaging sampling time) discarded, time subtracted remaining time values, time maximum observed mean value new time zero. solution_type set \"eigen\", solution system differential equations based spectral decomposition coefficient matrix cases possible. set \"deSolve\", numerical ode solver package deSolve used. set \"analytical\", analytical solution model used. implemented relatively simple degradation models.  default \"auto\", uses \"analytical\" possible, otherwise \"deSolve\" compiler present, \"eigen\" compiler present model can expressed using eigenvalues eigenvectors. method.ode solution method passed via mkinpredict() deSolve::ode() case solution type \"deSolve\". default \"lsoda\" performant, sometimes fails converge. use_compiled set FALSE, compiled version mkinmod model used calls mkinpredict() even compiled version present. control list control arguments passed stats::nlminb(). transform_rates Boolean specifying kinetic rate constants transformed model specification used fitting better compliance assumption normal distribution estimator. TRUE, also alpha beta parameters FOMC model log-transformed, well k1 k2 rate constants DFOP HS models break point tb HS model.  FALSE, zero used lower bound rates optimisation. transform_fractions Boolean specifying formation fractions transformed model specification used fitting better compliance assumption normal distribution estimator. default (TRUE) transformations. TRUE, g parameter DFOP model also transformed. Transformations described transform_odeparms. quiet Suppress printing current value negative log-likelihood improvement? atol Absolute error tolerance, passed deSolve::ode(). Default 1e-8, lower default deSolve::lsoda() function used per default. rtol Absolute error tolerance, passed deSolve::ode(). Default 1e-10, much lower deSolve::lsoda(). error_model error model \"const\", constant standard deviation assumed. error model \"obs\", observed variable assumed variance. error model \"tc\" (two-component error model), two component error model similar one described Rocke Lorenzato (1995) used setting likelihood function.  Note model deviates model Rocke Lorenzato, model implies errors follow lognormal distribution large values, normal distribution assumed method. error_model_algorithm \"auto\", selected algorithm depends error model.  error model \"const\", unweighted nonlinear least squares fitting (\"OLS\") selected. error model \"obs\", \"tc\", \"d_3\" algorithm selected. algorithm \"d_3\" directly minimize negative log-likelihood independently also use three step algorithm described . fit higher likelihood returned. algorithm \"direct\" directly minimize negative log-likelihood. algorithm \"twostep\" minimize negative log-likelihood initial unweighted least squares optimisation step. algorithm \"threestep\" starts unweighted least squares, optimizes error model using degradation model parameters found, minimizes negative log-likelihood free degradation error model parameters. algorithm \"fourstep\" starts unweighted least squares, optimizes error model using degradation model parameters found, optimizes degradation model fixed error model parameters, finally minimizes negative log-likelihood free degradation error model parameters. algorithm \"IRLS\" (Iteratively Reweighted Least Squares) starts unweighted least squares, iterates optimization error model parameters subsequent optimization degradation model using error model parameters, error model parameters converge. reweight.tol Tolerance convergence criterion calculated error model parameters IRLS fits. reweight.max.iter Maximum number iterations IRLS fits. trace_parms trace parameter values listed? test_residuals residuals tested normal distribution? ... arguments passed deSolve::ode().","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"list \"mkinfit\" class attribute.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Per default, parameters kinetic models internally transformed order better satisfy assumption normal distribution estimators.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"using \"IORE\" submodel metabolites, fitting \"transform_rates = TRUE\" (default) often leads failures numerical ODE solver. situation may help switch internal rate transformation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Rocke DM Lorenzato S (1995) two-component model measurement error analytical chemistry. Technometrics 37(2), 176-184. Ranke J Meinecke S (2019) Error Models Kinetic Evaluation Chemical Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124 .","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"","code":"# Use shorthand notation for parent only degradation fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE) summary(fit) #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:22:04 2025  #> Date of summary: Mon May 12 20:22:04 2025  #>  #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #>  #> Model predictions using solution type analytical  #>  #> Fitted using 222 model solutions performed in 0.027 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>          value   type #> parent_0  85.1  state #> alpha      1.0 deparm #> beta      10.0 deparm #>  #> Starting values for the transformed parameters actually optimised: #>               value lower upper #> parent_0  85.100000  -Inf   Inf #> log_alpha  0.000000  -Inf   Inf #> log_beta   2.302585  -Inf   Inf #>  #> Fixed parameter values: #> None #>  #> Results: #>  #>        AIC      BIC    logLik #>   44.68652 45.47542 -18.34326 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>           Estimate Std. Error    Lower   Upper #> parent_0  85.87000     1.8070 81.23000 90.5200 #> log_alpha  0.05192     0.1353 -0.29580  0.3996 #> log_beta   0.65100     0.2287  0.06315  1.2390 #> sigma      1.85700     0.4378  0.73200  2.9830 #>  #> Parameter correlation: #>             parent_0  log_alpha   log_beta     sigma #> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.681e-08 #> log_alpha -1.565e-01  1.000e+00  9.564e-01 1.013e-07 #> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.637e-08 #> sigma      4.681e-08  1.013e-07  8.637e-08 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   85.870  47.530 3.893e-08 81.2300 90.520 #> alpha       1.053   7.393 3.562e-04  0.7439  1.491 #> beta        1.917   4.373 3.601e-03  1.0650  3.451 #> sigma       1.857   4.243 4.074e-03  0.7320  2.983 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   6.657       3  6 #> parent     6.657       3  6 #>  #> Estimated disappearance times: #>         DT50  DT90 DT50back #> parent 1.785 15.15     4.56 #>  #> Data: #>  time variable observed predicted residual #>     0   parent     85.1    85.875  -0.7749 #>     1   parent     57.9    55.191   2.7091 #>     3   parent     29.9    31.845  -1.9452 #>     7   parent     14.6    17.012  -2.4124 #>    14   parent      9.7     9.241   0.4590 #>    28   parent      6.6     4.754   1.8460 #>    63   parent      4.0     2.102   1.8977 #>    91   parent      3.9     1.441   2.4590 #>   119   parent      0.6     1.092  -0.4919  # One parent compound, one metabolite, both single first order. # We remove zero values from FOCUS dataset D in order to avoid warnings FOCUS_D <- subset(FOCUS_2006_D, value != 0) # Use mkinsub for convenience in model formulation. Pathway to sink included per default. SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded  # Fit the model quietly to the FOCUS example dataset D using defaults fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) plot_sep(fit)  # As lower parent values appear to have lower variance, we try an alternative error model fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\") # This avoids the warning, and the likelihood ratio test confirms it is preferable lrtest(fit.tc, fit) #> Likelihood ratio test #>  #> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -64.983                          #> 2   5 -97.224 -1 64.483  9.737e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # We can also allow for different variances of parent and metabolite as error model fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"obs\") # The two-component error model has significantly higher likelihood lrtest(fit.obs, fit.tc) #> Likelihood ratio test #>  #> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -64.983                          #> 2   6 -96.936  0 63.907  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 parms(fit.tc) #>       parent_0       k_parent           k_m1 f_parent_to_m1      sigma_low  #>   1.007343e+02   1.005562e-01   5.166712e-03   5.083933e-01   3.049883e-03  #>       rsd_high  #>   7.928118e-02  endpoints(fit.tc) #> $ff #>   parent_m1 parent_sink  #>   0.5083933   0.4916067  #>  #> $distimes #>             DT50      DT90 #> parent   6.89313  22.89848 #> m1     134.15634 445.65771 #>   # We can show a quick (only one replication) benchmark for this case, as we # have several alternative solution methods for the model. We skip # uncompiled deSolve, as it is so slow. More benchmarks are found in the # benchmark vignette # \\dontrun{ if(require(rbenchmark)) {   benchmark(replications = 1, order = \"relative\", columns = c(\"test\", \"relative\", \"elapsed\"),     deSolve_compiled = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"deSolve\", use_compiled = TRUE),     eigen = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"eigen\"),     analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"analytical\")) } #>               test relative elapsed #> 3       analytical    1.000   0.256 #> 2            eigen    1.898   0.486 #> 1 deSolve_compiled    2.000   0.512 # }  # Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO # \\dontrun{ FOMC_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE) # Again, we get a warning and try a more sophisticated error model fit.FOMC_SFO.tc <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\") # This model has a higher likelihood, but not significantly so lrtest(fit.tc, fit.FOMC_SFO.tc) #> Likelihood ratio test #>  #> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq) #> 1   7 -64.829                      #> 2   6 -64.983 -1 0.3075     0.5792 # Also, the missing standard error for log_beta and the t-tests for alpha # and beta indicate overparameterisation summary(fit.FOMC_SFO.tc, data = FALSE) #> Warning: NaNs produced #> Warning: diag(V) had non-positive or NA entries; the non-finite result may be dubious #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:22:10 2025  #> Date of summary: Mon May 12 20:22:10 2025  #>  #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) * #>            parent - k_m1 * m1 #>  #> Model predictions using solution type deSolve  #>  #> Fitted using 4062 model solutions performed in 0.809 s #>  #> Error model: Two-component variance function  #>  #> Error model algorithm: d_3  #> Direct fitting and three-step fitting yield approximately the same likelihood  #>  #> Starting values for parameters to be optimised: #>                 value   type #> parent_0       100.75  state #> alpha            1.00 deparm #> beta            10.00 deparm #> k_m1             0.10 deparm #> f_parent_to_m1   0.50 deparm #> sigma_low        0.10  error #> rsd_high         0.10  error #>  #> Starting values for the transformed parameters actually optimised: #>                      value lower upper #> parent_0        100.750000  -Inf   Inf #> log_k_m1         -2.302585  -Inf   Inf #> f_parent_qlogis   0.000000  -Inf   Inf #> log_alpha         0.000000  -Inf   Inf #> log_beta          2.302585  -Inf   Inf #> sigma_low         0.100000     0   Inf #> rsd_high          0.100000     0   Inf #>  #> Fixed parameter values: #>      value  type #> m1_0     0 state #>  #> Results: #>  #>       AIC      BIC    logLik #>   143.658 155.1211 -64.82902 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>                   Estimate Std. Error     Lower      Upper #> parent_0        101.600000  2.6400000 96.240000 107.000000 #> log_k_m1         -5.284000  0.0929100 -5.474000  -5.095000 #> f_parent_qlogis   0.001426  0.0766900 -0.155000   0.157800 #> log_alpha         5.522000  0.0077320  5.506000   5.538000 #> log_beta          7.806000        NaN       NaN        NaN #> sigma_low         0.002488  0.0002431  0.001992   0.002984 #> rsd_high          0.079210  0.0093280  0.060180   0.098230 #>  #> Parameter correlation: #>                  parent_0  log_k_m1 f_parent_qlogis log_alpha log_beta #> parent_0         1.000000 -0.095161        -0.76675   0.70542      NaN #> log_k_m1        -0.095161  1.000000         0.51429  -0.14382      NaN #> f_parent_qlogis -0.766750  0.514286         1.00000  -0.61393      NaN #> log_alpha        0.705417 -0.143821        -0.61393   1.00000      NaN #> log_beta              NaN       NaN             NaN       NaN        1 #> sigma_low        0.016086  0.001583         0.01547   5.87036      NaN #> rsd_high         0.006618 -0.011695        -0.05356   0.04848      NaN #>                 sigma_low  rsd_high #> parent_0         0.016086  0.006618 #> log_k_m1         0.001583 -0.011695 #> f_parent_qlogis  0.015466 -0.053560 #> log_alpha        5.870361  0.048483 #> log_beta              NaN       NaN #> sigma_low        1.000000 -0.652545 #> rsd_high        -0.652545  1.000000 #>  #> 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       1.016e+02 32.7800 6.310e-26 9.624e+01 1.070e+02 #> k_m1           5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03 #> f_parent_to_m1 5.004e-01 20.8300 4.316e-20 4.613e-01 5.394e-01 #> alpha          2.502e+02  0.5624 2.889e-01 2.463e+02 2.542e+02 #> beta           2.455e+03  0.5549 2.915e-01        NA        NA #> sigma_low      2.488e-03  0.4843 3.158e-01 1.992e-03 2.984e-03 #> rsd_high       7.921e-02  8.4300 8.001e-10 6.018e-02 9.823e-02 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   6.781       5 14 #> parent     7.141       3  6 #> m1         4.640       2  8 #>  #> Resulting formation fractions: #>                 ff #> parent_m1   0.5004 #> parent_sink 0.4996 #>  #> Estimated disappearance times: #>           DT50  DT90 DT50back #> parent   6.812  22.7    6.834 #> m1     136.661 454.0       NA  # We can easily use starting parameters from the parent only fit (only for illustration) fit.FOMC = mkinfit(\"FOMC\", FOCUS_2006_D, quiet = TRUE, error_model = \"tc\") fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE,   parms.ini = fit.FOMC$bparms.ode, error_model = \"tc\") # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to set up a kinetic model with one or more state variables — mkinmod","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"function usually called using call mkinsub() observed variable, specifying corresponding submodel well outgoing pathways (see examples). Print mkinmod objects way user finds way get components.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"","code":"mkinmod(   ...,   use_of_ff = \"max\",   name = NULL,   speclist = NULL,   quiet = FALSE,   verbose = FALSE,   dll_dir = NULL,   unload = FALSE,   overwrite = FALSE )  # S3 method for class 'mkinmod' print(x, ...)  mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"... observed variable, list obtained mkinsub() specified argument (see examples).  Currently, single first order kinetics \"SFO\", indeterminate order rate equation kinetics \"IORE\", single first order reversible binding \"SFORB\" implemented variables, \"FOMC\", \"DFOP\", \"HS\" \"logistic\" can additionally chosen first variable assumed source compartment. Additionally, mkinsub() argument , specifying names variables transfer assumed model. argument use_of_ff set \"min\" model compartment \"SFO\" \"SFORB\", additional mkinsub() argument can sink = FALSE, effectively fixing flux sink zero. print.mkinmod, argument currently used. use_of_ff Specification use formation fractions model equations , applicable, coefficient matrix.  \"max\", formation fractions always used (default).  \"min\", minimum use formation fractions made, .e. first-order pathway metabolite rate constant. name name model. valid R object name. speclist specification observed variables submodel types pathways can given single list using argument. Default NULL. quiet messages suppressed? verbose TRUE, passed inline::cfunction() applicable give detailed information C function built. dll_dir Directory DLL object, generated internally inline::cfunction(), saved.  DLL stored permanent location use future sessions, 'dll_dir' 'name' specified. helpful fit objects cached e.g. knitr, cache remains functional across sessions DLL stored user defined location. unload DLL target location 'dll_dir' already loaded, unloaded first? overwrite file exists target DLL location 'dll_dir', overwritten? x mkinmod object. submodel Character vector length one specify submodel type. See mkinmod list allowed submodel names. Vector names state variable transformation shall included model. sink pathway sink included model addition pathways state variables? full_name optional name used e.g. plotting fits performed model.  can use non-ASCII characters , R code portable, .e. may produce unintended plot results operating systems system configurations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"list class mkinmod use mkinfit(), containing, among others, diffs vector string representations differential equations, one modelling variable. map list containing named character vectors observed variable, specifying modelling variables represented. use_of_ff content use_of_ff passed list component. deg_func generated, function containing solution degradation model. coefmat coefficient matrix, system differential equations can represented one. cf generated, compiled function calculating derivatives returned cfunction. list use mkinmod.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"definition model types parameters, equations given FOCUS NAFTA guidance documents used. kinetic models one observed variable, symbolic solution system differential equations included resulting mkinmod object cases, speeding solution. C compiler found pkgbuild::has_compiler() one observed variable specification, C code generated evaluating differential equations, compiled using inline::cfunction() added resulting mkinmod object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"IORE submodel well tested metabolites. using model metabolites, may want read note help page mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics NAFTA Technical Working Group Pesticides (dated) Guidance Evaluating Calculating Degradation Kinetics Environmental Media","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"","code":"# Specify the SFO model (this is not needed any more, as we can now mkinfit(\"SFO\", ...) SFO <- mkinmod(parent = mkinsub(\"SFO\"))  # One parent compound, one metabolite, both single first order SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(SFO_SFO) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $parent #> $type: SFO; $to: m1; $sink: TRUE #> $m1 #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_parent/dt = - k_parent * parent #> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1  # \\dontrun{  fit_sfo_sfo <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, solution_type = \"deSolve\") #> Warning: Observations with value of zero were removed from the data   # Now supplying compound names used for plotting, and write to user defined location  # We need to choose a path outside the session tempdir because this gets removed  DLL_dir <- \"~/.local/share/mkin\"  if (!dir.exists(DLL_dir)) dir.create(DLL_dir)  SFO_SFO.2 <- mkinmod(    parent = mkinsub(\"SFO\", \"m1\", full_name = \"Test compound\"),    m1 = mkinsub(\"SFO\", full_name = \"Metabolite M1\"),    name = \"SFO_SFO\", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE) #> Temporary DLL for differentials generated and loaded #> Copied DLL from /tmp/Rtmph4D0tb/file1385e349a06fa9.so to /home/jranke/.local/share/mkin/SFO_SFO.so # Now we can save the model and restore it in a new session saveRDS(SFO_SFO.2, file = \"~/SFO_SFO.rds\") # Terminate the R session here if you would like to check, and then do library(mkin) SFO_SFO.3 <- readRDS(\"~/SFO_SFO.rds\") fit_sfo_sfo <- mkinfit(SFO_SFO.3, FOCUS_2006_D, quiet = TRUE, solution_type = \"deSolve\") #> Warning: Observations with value of zero were removed from the data  # Show details of creating the C function SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\"), verbose = TRUE) #> Setting PKG_CPPFLAGS to -DR_NO_REMAP  #> Program source: #>   1: #include <R.h> #>   2:  #>   3:  #>   4: static double parms [3]; #>   5: #define k_parent parms[0] #>   6: #define f_parent_to_m1 parms[1] #>   7: #define k_m1 parms[2] #>   8:  #>   9: void initpar(void (* odeparms)(int *, double *)) { #>  10:     int N = 3; #>  11:     odeparms(&N, parms); #>  12: } #>  13:  #>  14:  #>  15: void diffs ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) { #>  16:  #>  17: f[0] = - k_parent * y[0]; #>  18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1]; #>  19: } #> Temporary DLL for differentials generated and loaded  # The symbolic solution which is available in this case is not # made for human reading but for speed of computation SFO_SFO$deg_func #> function (observed, odeini, odeparms)  #> { #>     predicted <- numeric(0) #>     with(as.list(odeparms), { #>         t <- observed[observed$name == \"parent\", \"time\"] #>         predicted <<- c(predicted, SFO.solution(t, odeini[\"parent\"],  #>             k_parent)) #>         t <- observed[observed$name == \"m1\", \"time\"] #>         predicted <<- c(predicted, (((k_m1 - k_parent) * odeini[\"m1\"] -  #>             f_parent_to_m1 * k_parent * odeini[\"parent\"]) * exp(-k_m1 *  #>             t) + f_parent_to_m1 * k_parent * odeini[\"parent\"] *  #>             exp(-k_parent * t))/(k_m1 - k_parent)) #>     }) #>     return(predicted) #> } #> <environment: 0x5555585b89c0>  # If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par <- mkinmod(  parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),  M1 = mkinsub(\"SFO\"),  M2 = mkinsub(\"SFO\"),  quiet = TRUE)  fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par,   synthetic_data_for_UBA_2014[[12]]$data,   quiet = TRUE) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"function plots confidence intervals parameters fitted using mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"","code":"mkinparplot(object)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"object fit represented mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"","code":"# \\dontrun{ model <- mkinmod(   T245 = mkinsub(\"SFO\", to = c(\"phenol\"), sink = FALSE),   phenol = mkinsub(\"SFO\", to = c(\"anisole\")),   anisole = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, subset(mccall81_245T, soil == \"Commerce\"), quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinparplot(fit)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"Deprecated function. now calls plot method plot.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"","code":"mkinplot(fit, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"fit object class mkinfit. ... arguments passed plot.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":null,"dir":"Reference","previous_headings":"","what":"Produce predictions from a kinetic model using specific parameters — mkinpredict","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"function produces time series observed variables kinetic model specified mkinmod, using specific set kinetic parameters initial values state variables.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"","code":"mkinpredict(x, odeparms, odeini, outtimes, ...)  # S3 method for class 'mkinmod' mkinpredict(   x,   odeparms = c(k_parent_sink = 0.1),   odeini = c(parent = 100),   outtimes = seq(0, 120, by = 0.1),   solution_type = \"deSolve\",   use_compiled = \"auto\",   use_symbols = FALSE,   method.ode = \"lsoda\",   atol = 1e-08,   rtol = 1e-10,   maxsteps = 20000L,   map_output = TRUE,   na_stop = TRUE,   ... )  # S3 method for class 'mkinfit' mkinpredict(   x,   odeparms = x$bparms.ode,   odeini = x$bparms.state,   outtimes = seq(0, 120, by = 0.1),   solution_type = \"deSolve\",   use_compiled = \"auto\",   method.ode = \"lsoda\",   atol = 1e-08,   rtol = 1e-10,   map_output = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"x kinetic model produced mkinmod, kinetic fit fitted mkinfit. latter case, fitted parameters used prediction. odeparms numeric vector specifying parameters used kinetic model, generally defined set ordinary differential equations. odeini numeric vector containing initial values state variables model. Note state variables can differ observed variables, example case SFORB model. outtimes numeric vector specifying time points model predictions generated. ... arguments passed ode solver case solver used. solution_type method used producing predictions. generally \"analytical\" one observed variable, usually \"deSolve\" case several observed variables. third possibility \"eigen\" fast comparison uncompiled ODE models, applicable models, e.g. using FOMC parent compound. use_compiled set FALSE, compiled version mkinmod model used, even present. use_symbols set TRUE (default), symbol info present mkinmod object used available accessing compiled code method.ode solution method passed via mkinpredict deSolve::ode() case solution type \"deSolve\" using compiled code. using compiled code, lsoda supported. atol Absolute error tolerance, passed ode solver. rtol Absolute error tolerance, passed ode solver. maxsteps Maximum number steps, passed ode solver. map_output Boolean specify output list values observed variables (default) state variables (set FALSE). Setting FALSE effect analytical solutions, always return mapped output. na_stop error deSolve::ode() returns NaN values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"matrix numeric solution wide format","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"","code":"SFO <- mkinmod(degradinol = mkinsub(\"SFO\")) # Compare solution types mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"analytical\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108997 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"deSolve\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108996 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"deSolve\", use_compiled = FALSE) #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108996 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"eigen\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108997 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752  # Compare integration methods to analytical solution mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"analytical\")[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"lsoda\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"ode45\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"rk4\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2480043  # rk4 is not as precise here  # The number of output times used to make a lot of difference until the # default for atol was adjusted mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),       seq(0, 20, by = 0.1))[201,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),       seq(0, 20, by = 0.01))[2001,] #>       time degradinol  #> 20.0000000  0.2478752   # Comparison of the performance of solution types SFO_SFO = mkinmod(parent = list(type = \"SFO\", to = \"m1\"),                   m1 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded if(require(rbenchmark)) {   benchmark(replications = 10, order = \"relative\", columns = c(\"test\", \"relative\", \"elapsed\"),     eigen = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"eigen\")[201,],     deSolve_compiled = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"deSolve\")[201,],     deSolve = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"deSolve\", use_compiled = FALSE)[201,],     analytical = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"analytical\", use_compiled = FALSE)[201,]) } #>               test relative elapsed #> 2 deSolve_compiled      1.0   0.002 #> 4       analytical      1.0   0.002 #> 1            eigen      4.5   0.009 #> 3          deSolve     35.0   0.070  # \\dontrun{   # Predict from a fitted model   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = \"deSolve\")   head(mkinpredict(f)) #> Error in !is.null(x$symbols) & use_symbols: operations are possible only for numeric, logical or complex types # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot residuals stored in an mkin object — mkinresplot","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"function plots residuals specified subset observed variables mkinfit object. combined plot fitted model residuals can obtained using plot.mkinfit using argument show_residuals = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"","code":"mkinresplot(   object,   obs_vars = names(object$mkinmod$map),   xlim = c(0, 1.1 * max(object$data$time)),   standardized = FALSE,   xlab = \"Time\",   ylab = ifelse(standardized, \"Standardized residual\", \"Residual\"),   maxabs = \"auto\",   legend = TRUE,   lpos = \"topright\",   col_obs = \"auto\",   pch_obs = \"auto\",   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"object fit represented mkinfit object. obs_vars character vector names observed variables residuals plotted. Defaults observed variables model xlim plot range x direction. standardized residuals standardized dividing standard deviation given error model fit? xlab Label x axis. ylab Label y axis. maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". legend legend plotted? lpos legend placed? Default \"topright\". passed legend. col_obs Colors observed variables. pch_obs Symbols used observed variables. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"Johannes Ranke Katrin Lindenberger","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"","code":"model <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinresplot(fit, \"m1\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"function calls mkinfit combinations models datasets specified first two arguments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"","code":"mmkin(   models = c(\"SFO\", \"FOMC\", \"DFOP\"),   datasets,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL,   ... )  # S3 method for class 'mmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"models Either character vector shorthand names like c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\", \"SFORB\"), optionally named list mkinmod objects. datasets optionally named list datasets suitable observed data mkinfit. cores number cores used multicore processing. used cluster argument NULL. Windows machines, cores > 1 supported, need use cluster argument use multiple logical processors. Per default, cores detected parallel::detectCores() used, except Windows default 1. cluster cluster returned makeCluster used parallel execution. ... used. x mmkin object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"two-dimensional array mkinfit objects /try-errors can indexed using model names first index (row index) dataset names second index (column index).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"","code":"# \\dontrun{ m_synth_SFO_lin <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                            M1 = mkinsub(\"SFO\", \"M2\"),                            M2 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_FOMC_lin <- mkinmod(parent = mkinsub(\"FOMC\", \"M1\"),                             M1 = mkinsub(\"SFO\", \"M2\"),                             M2 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin) datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data) names(datasets) <- paste(\"Dataset\", 1:3)  time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE)) time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))  time_default #>    user  system elapsed  #>   2.423   1.112   1.111  time_1 #>    user  system elapsed  #>   2.655   0.036   2.691   endpoints(fits.0[[\"SFO_lin\", 2]]) #> $ff #>   parent_M1 parent_sink       M1_M2     M1_sink  #>   0.7340481   0.2659519   0.7505690   0.2494310  #>  #> $distimes #>              DT50       DT90 #> parent  0.8777689   2.915885 #> M1      2.3257403   7.725942 #> M2     33.7201060 112.015767 #>   # plot.mkinfit handles rows or columns of mmkin result objects plot(fits.0[1, ])  plot(fits.0[1, ], obs_var = c(\"M1\", \"M2\"))  plot(fits.0[, 1])  # Use double brackets to extract a single mkinfit object, which will be plotted # by plot.mkinfit and can be plotted using plot_sep plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)  plot_sep(fits.0[[1, 1]]) # Plotting with mmkin (single brackets, extracting an mmkin object) does not # allow to plot the observed variables separately plot(fits.0[1, 1])   # On Windows, we can use multiple cores by making a cluster first cl <- parallel::makePSOCKcluster(12) f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"),   list(A = FOCUS_2006_A, B = FOCUS_2006_B, C = FOCUS_2006_C, D = FOCUS_2006_D),   cluster = cl, quiet = TRUE) print(f) #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  A  B  C  D  #>   SFO  OK OK OK OK #>   FOMC C  OK OK OK #>   DFOP OK OK OK OK #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings # We get false convergence for the FOMC fit to FOCUS_2006_A because this # dataset is really SFO, and the FOMC fit is overparameterised parallel::stopCluster(cl) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a hierarchical model fit with multiple starting values — multistart","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"purpose method check certain algorithm fitting nonlinear hierarchical models (also known nonlinear mixed-effects models) reliably yield results sufficiently similar , started certain range reasonable starting parameters. inspired article practical identifiabiliy frame nonlinear mixed-effects models Duchesne et al (2021).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"","code":"multistart(   object,   n = 50,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL,   ... )  # S3 method for class 'saem.mmkin' multistart(object, n = 50, cores = 1, cluster = NULL, ...)  # S3 method for class 'multistart' print(x, ...)  best(object, ...)  # Default S3 method best(object, ...)  which.best(object, ...)  # Default S3 method which.best(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"object fit object work n many different combinations starting parameters used? cores many fits run parallel (posix platforms)? cluster cluster returned parallel::makeCluster used parallel execution. ... Passed update function. x multistart object print","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"list saem.mmkin objects, class attributes 'multistart.saem.mmkin' 'multistart'. object highest likelihood index object highest likelihood","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"Duchesne R, Guillemin , Gandrillon O, Crauste F. Practical identifiability frame nonlinear mixed effects models: example vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. doi: 10.1186/s12859-021-04373-4.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"","code":"# \\dontrun{ library(mkin) dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- dmta_ds[[\"Elliot 2\"]] <- NULL  f_mmkin <- mmkin(\"DFOP\", dmta_ds, error_model = \"tc\", cores = 7, quiet = TRUE) f_saem_full <- saem(f_mmkin) f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16) parplot(f_saem_full_multi, lpos = \"topleft\", las = 2)  illparms(f_saem_full) #> [1] \"sd(log_k2)\"  f_saem_reduced <- update(f_saem_full, no_random_effect = \"log_k2\") illparms(f_saem_reduced) # On Windows, we need to create a PSOCK cluster first and refer to it # in the call to multistart() library(parallel) cl <- makePSOCKcluster(12) f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl) parplot(f_saem_reduced_multi, lpos = \"topright\", ylim = c(0.5, 2), las = 2)  stopCluster(cl) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate parent kinetics using the NAFTA guidance — nafta","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"function fits SFO, IORE DFOP models using mmkin returns object class nafta methods printing plotting. Print nafta objects. results three models printed order increasing model complexity, .e. SFO, IORE, finally DFOP.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"","code":"nafta(ds, title = NA, quiet = FALSE, ...)  # S3 method for class 'nafta' print(x, quiet = TRUE, digits = 3, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics environmental media. NAFTA Technical Working Group Pesticides https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance Calculate Representative Half-life Values Characterizing Pesticide Degradation https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"ds dataframe must contain one variable called \"time\" time values specified time argument, one column called \"name\" grouping observed values, finally one column observed values called \"value\". title Optional title dataset quiet evaluation text shown? ... arguments passed mmkin (printing method). x nafta object. digits Number digits used printing parameters dissipation times.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"list class nafta. list element named \"mmkin\" mmkin object containing fits three models. list element named \"title\" contains title dataset used. list element \"data\" contains dataset used fits.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"","code":"nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> 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   print(nafta_evaluation) #> Sums of squares: #>       SFO      IORE      DFOP  #> 1378.6832  615.7730  517.8836  #>  #> Critical sum of squares for checking the SFO model: #> [1] 717.4598 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)    Lower   Upper #> parent_0  83.7558 1.80e-14 77.18268 90.3288 #> k_parent   0.0017 7.43e-05  0.00112  0.0026 #> sigma      8.7518 1.22e-05  5.64278 11.8608 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       9.69e+01     NA 8.88e+01 1.05e+02 #> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09 #> N_parent       6.68e+00     NA 4.19e+00 9.17e+00 #> sigma          5.85e+00     NA 3.76e+00 7.94e+00 #>  #> $DFOP #>          Estimate   Pr(>t)    Lower    Upper #> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 #> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02 #> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03 #> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01 #> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00 #>  #>  #> DTx values: #>      DT50    DT90 DT50_rep #> SFO   407    1350      407 #> IORE  541 5190000  1560000 #> DFOP  429    2380      841 #>  #> Representative half-life: #> [1] 841.41   plot(nafta_evaluation)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions to create nlme models from mmkin row objects — nlme_function","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"functions facilitate setting nonlinear mixed effects model mmkin row object. mmkin row object essentially list mkinfit objects obtained fitting model list datasets. used internally nlme.mmkin() method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"","code":"nlme_function(object)  nlme_data(object)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"object mmkin row object containing several fits model different datasets","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"function can used nlme nlme::groupedData object","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"","code":"sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) m_SFO <- mkinmod(parent = mkinsub(\"SFO\")) d_SFO_1 <- mkinpredict(m_SFO,   c(k_parent = 0.1),   c(parent = 98), sampling_times) d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = \"time\") d_SFO_2 <- mkinpredict(m_SFO,   c(k_parent = 0.05),   c(parent = 102), sampling_times) d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = \"time\") d_SFO_3 <- mkinpredict(m_SFO,   c(k_parent = 0.02),   c(parent = 103), sampling_times) d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = \"time\")  d1 <- add_err(d_SFO_1, function(value) 3, n = 1) d2 <- add_err(d_SFO_2, function(value) 2, n = 1) d3 <- add_err(d_SFO_3, function(value) 4, n = 1) ds <- c(d1 = d1, d2 = d2, d3 = d3)  f <- mmkin(\"SFO\", ds, cores = 1, quiet = TRUE) mean_dp <- mean_degparms(f) grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by # pkgdown to generate the html docs. assign(\"nlme_f\", nlme_f, globalenv()) assign(\"grouped_data\", grouped_data, globalenv())  library(nlme) m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),   data = grouped_data,   fixed = parent_0 + log_k_parent_sink ~ 1,   random = pdDiag(parent_0 + log_k_parent_sink ~ 1),   start = mean_dp) summary(m_nlme) #> Nonlinear mixed-effects model fit by maximum likelihood #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink)  #>   Data: grouped_data  #>        AIC      BIC    logLik #>   266.6428 275.8935 -128.3214 #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #>  Level: ds #>  Structure: Diagonal #>             parent_0 log_k_parent_sink Residual #> StdDev: 0.0003775775         0.7058039 3.065183 #>  #> Fixed effects:  parent_0 + log_k_parent_sink ~ 1  #>                       Value Std.Error DF   t-value p-value #> parent_0          101.18323 0.7900461 43 128.07257       0 #> log_k_parent_sink  -3.08708 0.4171755 43  -7.39995       0 #>  Correlation:  #>                   prnt_0 #> log_k_parent_sink 0.031  #>  #> Standardized Within-Group Residuals: #>         Min          Q1         Med          Q3         Max  #> -2.38427070 -0.52059848  0.03593021  0.39987268  2.73188969  #>  #> Number of Observations: 47 #> Number of Groups: 3  plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))  # augPred does not work on fits with more than one state # variable # # The procedure is greatly simplified by the nlme.mmkin function f_nlme <- nlme(f) plot(f_nlme)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an nlme model for an mmkin row object — nlme.mmkin","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"functions sets nonlinear mixed effects model mmkin row object. mmkin row object essentially list mkinfit objects obtained fitting model list datasets.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"","code":"# S3 method for class 'mmkin' nlme(   model,   data = \"auto\",   fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =     paste(el, 1, sep = \"~\")))),   random = pdDiag(fixed),   groups,   start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),   correlation = NULL,   weights = NULL,   subset,   method = c(\"ML\", \"REML\"),   na.action = na.fail,   naPattern,   control = list(),   verbose = FALSE )  # S3 method for class 'nlme.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)  # S3 method for class 'nlme.mmkin' update(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"model mmkin row object. data Ignored, data taken mmkin model fixed Ignored, degradation parameters fitted mmkin model used fixed parameters random specified, correlations random effects set optimised degradation model parameters. achieved using nlme::pdDiag method. groups See documentation nlme start specified, mean values fitted degradation parameters taken mmkin object used correlation See documentation nlme weights passed nlme subset passed nlme method passed nlme na.action passed nlme naPattern passed nlme control passed nlme verbose passed nlme x nlme.mmkin object print digits Number digits use printing ... Update specifications passed update.nlme object nlme.mmkin object update","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"Upon success, fitted 'nlme.mmkin' object, nlme object additional elements. also inherits 'mixed.mmkin'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"Note convergence nlme algorithms depends quality data. degradation kinetics, often datasets (e.g. data soils) complicated degradation models, may make impossible obtain convergence nlme.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"object inherits nlme::nlme, wealth methods automatically work 'nlme.mmkin' objects, nlme::intervals(), nlme::anova.lme() nlme::coef.lme().","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"","code":"ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")], name == \"parent\"))  # \\dontrun{   f <- mmkin(c(\"SFO\", \"DFOP\"), ds, quiet = TRUE, cores = 1)   library(nlme)   f_nlme_sfo <- nlme(f[\"SFO\", ])   f_nlme_dfop <- nlme(f[\"DFOP\", ])   anova(f_nlme_sfo, f_nlme_dfop) #>             Model df      AIC      BIC    logLik   Test  L.Ratio p-value #> f_nlme_sfo      1  5 625.0539 637.5529 -307.5269                         #> f_nlme_dfop     2  9 495.1270 517.6253 -238.5635 1 vs 2 137.9269  <.0001   print(f_nlme_dfop) #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -238.6 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)  #> parent_0   log_k1   log_k2 g_qlogis  #>  94.1702  -1.8002  -4.1474   0.0324  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev:    2.488 0.8447   1.33   0.4652    2.321 #>    plot(f_nlme_dfop)    endpoints(f_nlme_dfop) #> $distimes #>            DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent 10.79857 100.7937 30.34192 4.193937 43.85442 #>     ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],    function(x) x$data[c(\"name\", \"time\", \"value\")])   m_sfo_sfo <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),     A1 = mkinsub(\"SFO\"), use_of_ff = \"min\", quiet = TRUE)   m_sfo_sfo_ff <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),     A1 = mkinsub(\"SFO\"), use_of_ff = \"max\", quiet = TRUE)   m_dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),     A1 = mkinsub(\"SFO\"), quiet = TRUE)    f_2 <- mmkin(list(\"SFO-SFO\" = m_sfo_sfo,    \"SFO-SFO-ff\" = m_sfo_sfo_ff,    \"DFOP-SFO\" = m_dfop_sfo),     ds_2, quiet = TRUE)    f_nlme_sfo_sfo <- nlme(f_2[\"SFO-SFO\", ])   plot(f_nlme_sfo_sfo)     # With formation fractions this does not coverge with defaults   # f_nlme_sfo_sfo_ff <- nlme(f_2[\"SFO-SFO-ff\", ])   #plot(f_nlme_sfo_sfo_ff)    # For the following, we need to increase pnlsMaxIter and the tolerance   # to get convergence   f_nlme_dfop_sfo <- nlme(f_2[\"DFOP-SFO\", ],     control = list(pnlsMaxIter = 120, tolerance = 5e-4))    plot(f_nlme_dfop_sfo)     anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) #>                 Model df       AIC      BIC    logLik   Test  L.Ratio p-value #> f_nlme_dfop_sfo     1 13  843.8547  884.620 -408.9273                         #> f_nlme_sfo_sfo      2  9 1085.1821 1113.404 -533.5910 1 vs 2 249.3274  <.0001    endpoints(f_nlme_sfo_sfo) #> $ff #> parent_sink   parent_A1     A1_sink  #>   0.5912432   0.4087568   1.0000000  #>  #> $distimes #>            DT50     DT90 #> parent 19.13518  63.5657 #> A1     66.02155 219.3189 #>    endpoints(f_nlme_dfop_sfo) #> $ff #>   parent_A1 parent_sink  #>   0.2768574   0.7231426  #>  #> $distimes #>             DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent  11.07091 104.6320 31.49737 4.462383 46.20825 #> A1     162.30550 539.1672       NA       NA       NA #>     if (length(findFunction(\"varConstProp\")) > 0) { # tc error model for nlme available     # Attempts to fit metabolite kinetics with the tc error model are possible,     # but need tweeking of control values and sometimes do not converge      f_tc <- mmkin(c(\"SFO\", \"DFOP\"), ds, quiet = TRUE, error_model = \"tc\")     f_nlme_sfo_tc <- nlme(f_tc[\"SFO\", ])     f_nlme_dfop_tc <- nlme(f_tc[\"DFOP\", ])     AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)     print(f_nlme_dfop_tc)   } #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -238.4 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)  #> parent_0   log_k1   log_k2 g_qlogis  #> 94.04774 -1.82340 -4.16716  0.05685  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev:    2.474   0.85  1.337   0.4659        1 #>  #> Variance function: #>  Structure: Constant plus proportion of variance covariate #>  Formula: ~fitted(.)  #>  Parameter estimates: #>      const       prop  #> 2.23222933 0.01262399     f_2_obs <- update(f_2, error_model = \"obs\")   f_nlme_sfo_sfo_obs <- nlme(f_2_obs[\"SFO-SFO\", ])   print(f_nlme_sfo_sfo_obs) #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent #> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -473 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1)  #>          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink  #>            87.976            -3.670            -4.164            -4.645  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual #> StdDev:    3.992             1.777           1.055        0.4821    6.483 #>  #> Variance function: #>  Structure: Different standard deviations per stratum #>  Formula: ~1 | name  #>  Parameter estimates: #>    parent        A1  #> 1.0000000 0.2050005    f_nlme_dfop_sfo_obs <- nlme(f_2_obs[\"DFOP-SFO\", ],     control = list(pnlsMaxIter = 120, tolerance = 5e-4))    f_2_tc <- update(f_2, error_model = \"tc\")   # f_nlme_sfo_sfo_tc <- nlme(f_2_tc[\"SFO-SFO\", ]) # No convergence with 50 iterations   # f_nlme_dfop_sfo_tc <- nlme(f_2_tc[\"DFOP-SFO\", ],   #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm    anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs) #>                     Model df      AIC     BIC    logLik   Test  L.Ratio p-value #> f_nlme_dfop_sfo         1 13 843.8547 884.620 -408.9273                         #> f_nlme_dfop_sfo_obs     2 14 817.5338 861.435 -394.7669 1 vs 2 28.32084  <.0001  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"Number observations mkinfit object fitted","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"","code":"# S3 method for class 'mkinfit' nobs(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"object mkinfit object ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"number rows data included mkinfit object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract model parameters — parms","title":"Extract model parameters — parms","text":"function returns degradation model parameters well error model parameters per default, order avoid working fitted model without considering error structure assumed fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract model parameters — parms","text":"","code":"parms(object, ...)  # S3 method for class 'mkinfit' parms(object, transformed = FALSE, errparms = TRUE, ...)  # S3 method for class 'mmkin' parms(object, transformed = FALSE, errparms = TRUE, ...)  # S3 method for class 'multistart' parms(object, exclude_failed = TRUE, ...)  # S3 method for class 'saem.mmkin' parms(object, ci = FALSE, covariates = NULL, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract model parameters — parms","text":"object fitted model object. ... used transformed parameters returned used internally optimisation? errparms error model parameters returned addition degradation parameters? exclude_failed multistart objects, rows failed fits removed returned parameter matrix? ci matrix estimates confidence interval boundaries returned? FALSE (default), vector estimates returned covariates given, otherwise matrix estimates returned, column corresponding row data frame holding covariates covariates data frame holding covariate values return parameter values. effect 'ci' FALSE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract model parameters — parms","text":"Depending object, numeric vector fitted model parameters, matrix (e.g. mmkin row objects), list matrices (e.g. mmkin objects one row).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract model parameters — parms","text":"","code":"# mkinfit objects fit <- mkinfit(\"SFO\", FOCUS_2006_C, quiet = TRUE) parms(fit) #>   parent_0   k_parent      sigma  #> 82.4921598  0.3060633  4.6730124  parms(fit, transformed = TRUE) #>     parent_0 log_k_parent        sigma  #>    82.492160    -1.183963     4.673012   # mmkin objects ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")])) names(ds) <- paste(\"Dataset\", 6:10) # \\dontrun{ fits <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), ds, quiet = TRUE, cores = 1) parms(fits[\"SFO\", ]) #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421 #> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673 parms(fits[, 2]) #> $SFO #>             Dataset 7 #> parent_0 82.666781678 #> k_parent  0.009647805 #> sigma     7.040168584 #>  #> $FOMC #>           Dataset 7 #> parent_0 92.6837649 #> alpha     0.4967832 #> beta     14.1451255 #> sigma     1.9167519 #>  #> $DFOP #>             Dataset 7 #> parent_0 91.058971584 #> k1        0.044946770 #> k2        0.002868336 #> g         0.526942414 #> sigma     2.221302196 #>  parms(fits) #> $SFO #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421 #> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673 #>  #> $FOMC #>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458 #> alpha     1.338667  0.4967832  1.639099  1.074460  0.2805272 #> beta     13.033315 14.1451255  5.007077  4.397126  6.9052224 #> sigma     1.847671  1.9167519  1.066063  3.146056  1.6222778 #>  #> $DFOP #>            Dataset 6    Dataset 7   Dataset 8   Dataset 9   Dataset 10 #> parent_0 96.55213663 91.058971584 90.34509493 98.14858820 94.311323735 #> k1        0.21954588  0.044946770  0.41232288  0.31697588  0.080663857 #> k2        0.02957934  0.002868336  0.07581766  0.03260384  0.003425417 #> g         0.44845068  0.526942414  0.66091967  0.65322767  0.342652880 #> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831 #>  parms(fits, transformed = TRUE) #> $SFO #>              Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0     88.522754 82.666782 86.854731 91.777931  82.148095 #> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737  -4.933090 #> sigma         5.152745  7.040169  3.676964  6.466923   6.504577 #>  #> $FOMC #>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10 #> parent_0  95.5585751 92.6837649 90.7197870 98.38393898  94.848146 #> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085 #> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278 #> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278 #>  #> $DFOP #>           Dataset 6  Dataset 7  Dataset 8  Dataset 9 Dataset 10 #> parent_0 96.5521366 91.0589716 90.3450949 98.1485882 94.3113237 #> log_k1   -1.5161940 -3.1022764 -0.8859486 -1.1489296 -2.5174647 #> log_k2   -3.5206791 -5.8540232 -2.5794240 -3.4233253 -5.6765322 #> g_qlogis -0.2069326  0.1078741  0.6673953  0.6332573 -0.6514943 #> sigma     1.3569047  2.2213022  1.3416908  2.8715985  1.9420678 #>  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot parameter variability of multistart objects — parplot","title":"Plot parameter variability of multistart objects — parplot","text":"Produces boxplot parameters multiple runs, scaled either parameters run highest likelihood, medians proposed paper Duchesne et al. (2021).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot parameter variability of multistart objects — parplot","text":"","code":"parplot(object, ...)  # S3 method for class 'multistart.saem.mmkin' parplot(   object,   llmin = -Inf,   llquant = NA,   scale = c(\"best\", \"median\"),   lpos = \"bottomleft\",   main = \"\",   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot parameter variability of multistart objects — parplot","text":"object multistart object ... Passed boxplot llmin minimum likelihood objects shown llquant Fractional value selecting fits higher likelihoods. Overrides 'llmin'. scale default, scale parameters using best available fit. 'median', parameters scaled using median parameters fits. lpos Positioning legend. main Title plot","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot parameter variability of multistart objects — parplot","text":"Starting values degradation model parameters error model parameters shown green circles. results obtained original run shown red circles.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot parameter variability of multistart objects — parplot","text":"Duchesne R, Guillemin , Gandrillon O, Crauste F. Practical identifiability frame nonlinear mixed effects models: example vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. doi: 10.1186/s12859-021-04373-4.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Plot predictions fitted nonlinear mixed model obtained via mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"","code":"# S3 method for class 'mixed.mmkin' plot(   x,   i = 1:ncol(x$mmkin),   obs_vars = names(x$mkinmod$map),   standardized = TRUE,   covariates = NULL,   covariate_quantiles = c(0.5, 0.05, 0.95),   xlab = \"Time\",   xlim = range(x$data$time),   resplot = c(\"predicted\", \"time\"),   pop_curves = \"auto\",   pred_over = NULL,   test_log_parms = FALSE,   conf.level = 0.6,   default_log_parms = NA,   ymax = \"auto\",   maxabs = \"auto\",   ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),   nrow.legend = ceiling((length(i) + 1)/ncol.legend),   rel.height.legend = 0.02 + 0.07 * nrow.legend,   rel.height.bottom = 1.1,   pch_ds = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],   col_ds = pch_ds + 1,   lty_ds = col_ds,   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"x object class mixed.mmkin, saem.mmkin nlme.mmkin numeric index select datasets plot individual predictions, case plots get large obs_vars character vector names observed variables data model plotted. Defauls observed variables model. standardized residuals standardized? takes effect resplot = \"time\". covariates Data frame covariate values variables covariate models object. given, overrides 'covariate_quantiles'. line data frame result line drawn population. Rownames used legend label lines. covariate_quantiles argument effect fitted object covariate models. , default show three population curves, 5th percentile, 50th percentile 95th percentile covariate values used fitting model. xlab Label x axis. xlim Plot range x direction. resplot residuals plotted time predicted values? pop_curves Per default, one population curve drawn case population parameters fitted model, e.g. saem objects. case covariate model, behaviour depends value 'covariates' pred_over Named list alternative predictions obtained mkinpredict compatible mkinmod. test_log_parms Passed mean_degparms case mixed.mmkin object conf.level Passed mean_degparms case mixed.mmkin object default_log_parms Passed mean_degparms case mixed.mmkin object ymax Vector maximum y axis values maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". ncol.legend Number columns use legend nrow.legend Number rows use legend rel.height.legend relative height legend shown top rel.height.bottom relative height bottom plot row pch_ds Symbols used plotting data. col_ds Colors used plotting observed data corresponding model prediction lines different datasets. lty_ds Line types used model predictions. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Covariate models currently supported saem.mmkin objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"","code":"ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) x$data[c(\"name\", \"time\", \"value\")]) names(ds) <- paste0(\"ds \", 6:10) dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),   A1 = mkinsub(\"SFO\"), quiet = TRUE) # \\dontrun{ f <- mmkin(list(\"DFOP-SFO\" = dfop_sfo), ds, quiet = TRUE) plot(f[, 3:4], standardized = TRUE)   # For this fit we need to increase pnlsMaxiter, and we increase the # tolerance in order to speed up the fit for this example evaluation # It still takes 20 seconds to run f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme)   f_saem <- saem(f, transformations = \"saemix\") plot(f_saem)   f_obs <- mmkin(list(\"DFOP-SFO\" = dfop_sfo), ds, quiet = TRUE, error_model = \"obs\") f_nlmix <- nlmix(f_obs) #> Error in nlmix(f_obs): could not find function \"nlmix\" plot(f_nlmix) #> Error: object 'f_nlmix' not found  # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo,   f_nlme$bparms.optim[-1],   c(parent = f_nlme$bparms.optim[[1]], A1 = 0),   seq(0, 180, by = 0.2)) plot(f_saem, pred_over = list(nlme = pred_nlme))  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"Solves differential equations optimised fixed parameters previous successful call mkinfit plots observed data together solution fitted model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"","code":"# S3 method for class 'mkinfit' plot(   x,   fit = x,   obs_vars = names(fit$mkinmod$map),   xlab = \"Time\",   ylab = \"Residue\",   xlim = range(fit$data$time),   ylim = \"default\",   col_obs = 1:length(obs_vars),   pch_obs = col_obs,   lty_obs = rep(1, length(obs_vars)),   add = FALSE,   legend = !add,   show_residuals = FALSE,   show_errplot = FALSE,   maxabs = \"auto\",   sep_obs = FALSE,   rel.height.middle = 0.9,   row_layout = FALSE,   lpos = \"topright\",   inset = c(0.05, 0.05),   show_errmin = FALSE,   errmin_digits = 3,   frame = TRUE,   ... )  plot_sep(   fit,   show_errmin = TRUE,   show_residuals = ifelse(identical(fit$err_mod, \"const\"), TRUE, \"standardized\"),   ... )  plot_res(   fit,   sep_obs = FALSE,   show_errmin = sep_obs,   standardized = ifelse(identical(fit$err_mod, \"const\"), FALSE, TRUE),   ... )  plot_err(fit, sep_obs = FALSE, show_errmin = sep_obs, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"x Alias fit introduced compatibility generic S3 method. fit object class mkinfit. obs_vars character vector names observed variables data model plotted. Defauls observed variables model. xlab Label x axis. ylab Label y axis. xlim Plot range x direction. ylim Plot range y direction. given list, plot ranges different plot rows can given row layout. col_obs Colors used plotting observed data corresponding model prediction lines. pch_obs Symbols used plotting data. lty_obs Line types used model predictions. add plot added existing plot? legend legend added plot? show_residuals residuals shown? one plot fits shown, residual plot lower third plot. Otherwise, .e. \"sep_obs\" given, residual plots located right plots fitted curves. set 'standardized', plot residuals divided standard deviation given fitted error model shown. show_errplot squared residuals error model shown? one plot fits shown, plot lower third plot.  Otherwise, .e. \"sep_obs\" given, residual plots located right plots fitted curves. maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". sep_obs observed variables shown separate subplots? yes, residual plots requested \"show_residuals\" shown next , plot fits. rel.height.middle relative height middle plot, two rows plots shown. row_layout use row layout residual plot error model plot shown right? lpos Position(s) legend(s). Passed legend first argument.  length one, length obs_var argument. inset Passed legend applicable. show_errmin FOCUS chi2 error value shown upper margin plot? errmin_digits number significant digits rounding FOCUS chi2 error percentage. frame frame drawn around plots? ... arguments passed plot. standardized calling 'plot_res', residuals standardized residual plot?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"current plot device tikz device, latex used formatting chi2 error level, show_errmin = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"","code":"# One parent compound, one metabolite, both single first order, path from # parent to sink included # \\dontrun{ SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"m1\", full = \"Parent\"),                    m1 = mkinsub(\"SFO\", full = \"Metabolite M1\" )) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = \"tc\") #> Warning: Observations with value of zero were removed from the data plot(fit)  plot_res(fit)  plot_res(fit, standardized = FALSE)  plot_err(fit)   # Show the observed variables separately, with residuals plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c(\"topright\", \"bottomright\"),      show_errmin = TRUE)   # The same can be obtained with less typing, using the convenience function plot_sep plot_sep(fit, lpos = c(\"topright\", \"bottomright\"))   # Show the observed variables separately, with the error model plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c(\"topright\", \"bottomright\"),      show_errmin = TRUE)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"x row selected mmkin object ([.mmkin), model fitted least one dataset shown. column, fit least one model dataset shown.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"","code":"# S3 method for class 'mmkin' plot(   x,   main = \"auto\",   legends = 1,   resplot = c(\"time\", \"errmod\"),   ylab = \"Residue\",   standardized = FALSE,   show_errmin = TRUE,   errmin_var = \"All data\",   errmin_digits = 3,   cex = 0.7,   rel.height.middle = 0.9,   ymax = \"auto\",   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"x object class mmkin, either one row one column. main main title placed outer margin plot. legends index fits legends shown. resplot residuals plotted time, using mkinresplot, squared residuals predicted values, error model, using mkinerrplot. ylab Label y axis. standardized residuals standardized? option passed mkinresplot, takes effect resplot = \"time\". show_errmin chi2 error level shown top plots left? errmin_var variable FOCUS chi2 error value shown. errmin_digits number significant digits rounding FOCUS chi2 error percentage. cex Passed plot functions mtext. rel.height.middle relative height middle plot, two rows plots shown. ymax Maximum y axis value plot.mkinfit. ... arguments passed plot.mkinfit mkinresplot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"current plot device tikz device, latex used formatting chi2 error level.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"","code":"# \\dontrun{   # Only use one core not to offend CRAN checks   fits <- mmkin(c(\"FOMC\", \"HS\"),                 list(\"FOCUS B\" = FOCUS_2006_B, \"FOCUS C\" = FOCUS_2006_C), # named list for titles                 cores = 1, quiet = TRUE, error_model = \"tc\") #> Warning: Optimisation did not converge: #> iteration limit reached without convergence (10)   plot(fits[, \"FOCUS C\"])    plot(fits[\"FOMC\", ])    plot(fits[\"FOMC\", ], show_errmin = FALSE)     # We can also plot a single fit, if we like the way plot.mmkin works, but then the plot   # height should be smaller than the plot width (this is not possible for the html pages   # generated by pkgdown, as far as I know).   plot(fits[\"FOMC\", \"FOCUS C\"]) # same as plot(fits[1, 2])     # Show the error models   plot(fits[\"FOMC\", ], resplot = \"errmod\")    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"plots ordered increasing complexity model function (SFO, IORE, DFOP).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"","code":"# S3 method for class 'nafta' plot(x, legend = FALSE, main = \"auto\", ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"x object class nafta. legend legend added? main Possibility override main title plot. ... arguments passed plot.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"Calls plot.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":null,"dir":"Reference","previous_headings":"","what":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"function imports one dataset sheet spreadsheet file. sheets selected based contents sheet 'Datasets', column called 'Dataset Number', containing numbers identifying dataset sheets read . second column must grouping variable, often named 'Soil'. Optionally, time normalization factors can given columns named 'Temperature' 'Moisture'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"","code":"read_spreadsheet(   path,   valid_datasets = \"all\",   parent_only = FALSE,   normalize = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"path Absolute relative path spreadsheet file valid_datasets Optional numeric index valid datasets, default use datasets parent_only parent data used? normalize time scale normalized using temperature moisture normalisation factors sheet 'Datasets'?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"must sheet 'Compounds', columns 'Name' 'Acronym'. first row read header read sheet assumed contain name acronym parent compound. dataset sheets named using dataset numbers read 'Datasets' sheet, .e. '1', '2', ... . dataset sheet, name observed variable (e.g. acronym parent compound one transformation products) first column, time values second colum, observed value third column. case relevant covariate data available, given sheet 'Covariates', containing one line value grouping variable specified 'Datasets'. values first column column must name second column 'Datasets'. Covariates read columns four higher. names preferably contain special characters like spaces, can easily used specifying covariate models. similar data structure defined R6 class mkindsg, probably complicated use.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. lmtest lrtest nlme intervals, nlme","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from an mkinfit model — residuals.mkinfit","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"Extract residuals mkinfit model","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"","code":"# S3 method for class 'mkinfit' residuals(object, standardized = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"object mkinfit object standardized residuals standardized dividing standard deviation obtained fitted error model? ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"","code":"f <- mkinfit(\"DFOP\", FOCUS_2006_C, quiet = TRUE) residuals(f) #> [1]  0.09726374 -0.13912142 -0.15351210  0.73388322 -0.08657004 -0.93204702 #> [7] -0.03269080  1.45347823 -0.88423697 residuals(f, standardized = TRUE) #> [1]  0.13969917 -0.19981904 -0.22048826  1.05407091 -0.12433989 -1.33869208 #> [7] -0.04695355  2.08761977 -1.27002287"},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit nonlinear mixed models with SAEM — saem","title":"Fit nonlinear mixed models with SAEM — saem","text":"function uses saemix::saemix() backend fitting nonlinear mixed effects models created mmkin row objects using Stochastic Approximation Expectation Maximisation algorithm (SAEM).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit nonlinear mixed models with SAEM — saem","text":"","code":"saem(object, ...)  # S3 method for class 'mmkin' saem(   object,   transformations = c(\"mkin\", \"saemix\"),   error_model = \"auto\",   degparms_start = numeric(),   test_log_parms = TRUE,   conf.level = 0.6,   solution_type = \"auto\",   covariance.model = \"auto\",   omega.init = \"auto\",   covariates = NULL,   covariate_models = NULL,   center_covariates = c(NA, \"median\", \"mean\"),   no_random_effect = NULL,   error.init = c(1, 1),   nbiter.saemix = c(300, 100),   control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,     save = FALSE, save.graphs = FALSE),   verbose = FALSE,   quiet = FALSE,   ... )  # S3 method for class 'saem.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)  saemix_model(   object,   solution_type = \"auto\",   transformations = c(\"mkin\", \"saemix\"),   error_model = \"auto\",   degparms_start = numeric(),   covariance.model = \"auto\",   no_random_effect = NULL,   omega.init = \"auto\",   covariates = NULL,   covariate_models = NULL,   error.init = numeric(),   test_log_parms = FALSE,   conf.level = 0.6,   verbose = FALSE,   ... )  saemix_data(object, covariates = NULL, verbose = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit nonlinear mixed models with SAEM — saem","text":"object mmkin row object containing several fits mkinmod model different datasets ... parameters passed saemix::saemixModel. transformations Per default, parameter transformations done mkin. argument set 'saemix', parameter transformations done 'saemix' supported cases, .e. (version 1.1.2) SFO, FOMC, DFOP HS without fixing parent_0, SFO DFOP one SFO metabolite. error_model Possibility override error model used mmkin object degparms_start Parameter values given named numeric vector used override starting values obtained 'mmkin' object. test_log_parms TRUE, attempt made use robust starting values population parameters fitted log parameters mkin (like rate constants) considering rate constants pass t-test calculating mean degradation parameters using mean_degparms. conf.level Possibility adjust required confidence level parameter tested requested 'test_log_parms'. solution_type Possibility specify solution type case automatic choice desired covariance.model passed saemix::saemixModel(). Per default, uncorrelated random effects specified degradation parameters. omega.init passed saemix::saemixModel(). using mkin transformations default covariance model optionally excluded random effects, variances degradation parameters estimated using mean_degparms, testing untransformed log parameters significant difference zero. using mkin transformations custom covariance model, default initialisation saemix::saemixModel used omega.init. covariates data frame covariate data use 'covariate_models', dataset names row names. covariate_models list containing linear model formulas one explanatory variable, .e. type 'parameter ~ covariate'. Covariates must available 'covariates' data frame. center_covariates Either NA, centering, preferred function calculating center, currently either median mean. no_random_effect Character vector degradation parameters variability groups. used covariance model explicitly specified. error.init passed saemix::saemixModel(). nbiter.saemix Convenience option increase number iterations control Passed saemix::saemix. verbose print information created objects type saemix::SaemixModel saemix::SaemixData? quiet suppress messages saemix prints beginning end optimisation process? x saem.mmkin object print digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit nonlinear mixed models with SAEM — saem","text":"S3 object class 'saem.mmkin', containing fitted saemix::SaemixObject list component named ''. object also inherits 'mixed.mmkin'. saemix::SaemixModel object. saemix::SaemixData object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit nonlinear mixed models with SAEM — saem","text":"mmkin row object essentially list mkinfit objects obtained fitting model list datasets using mkinfit. Starting values fixed effects (population mean parameters, argument psi0 saemix::saemixModel() mean values parameters found using mmkin.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit nonlinear mixed models with SAEM — saem","text":"","code":"# \\dontrun{ ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")])) names(ds) <- paste(\"Dataset\", 6:10) f_mmkin_parent_p0_fixed <- mmkin(\"FOMC\", ds,   state.ini = c(parent = 100), fixed_initials = \"parent\", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)  f_mmkin_parent <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent[\"SFO\", ]) f_saem_fomc <- saem(f_mmkin_parent[\"FOMC\", ]) f_saem_dfop <- saem(f_mmkin_parent[\"DFOP\", ]) anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik #> f_saem_sfo     5 624.33 622.38 -307.17 #> f_saem_fomc    7 467.85 465.11 -226.92 #> f_saem_dfop    9 493.76 490.24 -237.88 anova(f_saem_sfo, f_saem_dfop, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     #> f_saem_sfo     5 624.33 622.38 -307.17                          #> f_saem_dfop    9 493.76 490.24 -237.88 138.57  4  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 illparms(f_saem_dfop) #> [1] \"sd(g_qlogis)\" f_saem_dfop_red <- update(f_saem_dfop, no_random_effect = \"g_qlogis\") anova(f_saem_dfop, f_saem_dfop_red, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>                 npar    AIC    BIC     Lik Chisq Df Pr(>Chisq) #> f_saem_dfop_red    8 488.68 485.55 -236.34                     #> f_saem_dfop        9 493.76 490.24 -237.88     0  1          1  anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik #> f_saem_sfo     5 624.33 622.38 -307.17 #> f_saem_fomc    7 467.85 465.11 -226.92 #> f_saem_dfop    9 493.76 490.24 -237.88 # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) #> Loading required package: npde #> Package saemix, version 3.4, March 2024 #>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr #>  #> Attaching package: ‘saemix’ #> The following objects are masked from ‘package:npde’: #>  #>     kurtosis, skewness compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) #> Likelihoods calculated by importance sampling #>        AIC      BIC #> 1 624.3316 622.3788 #> 2 467.8472 465.1132 #> 3 493.7592 490.2441 plot(f_saem_fomc$so, plot.type = \"convergence\")  plot(f_saem_fomc$so, plot.type = \"individual.fit\") #> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde .  plot(f_saem_fomc$so, plot.type = \"npde\") #> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde . #> Please use npdeSaemix to obtain VPC and npde plot(f_saem_fomc$so, plot.type = \"vpc\")   f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = \"tc\") f_saem_fomc_tc <- saem(f_mmkin_parent_tc[\"FOMC\", ]) anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>                npar    AIC    BIC     Lik Chisq Df Pr(>Chisq) #> f_saem_fomc       7 467.85 465.11 -226.92                     #> f_saem_fomc_tc    8 469.90 466.77 -226.95     0  1          1  sfo_sfo <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fomc_sfo <- mkinmod(parent = mkinsub(\"FOMC\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, # and compiled ODEs for FOMC that are much slower f_mmkin <- mmkin(list(     \"SFO-SFO\" = sfo_sfo, \"FOMC-SFO\" = fomc_sfo, \"DFOP-SFO\" = dfop_sfo),   ds, quiet = TRUE) # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds # each on this system, as we use analytical solutions written for saemix. # When using the analytical solutions written for mkin this took around # four minutes f_saem_sfo_sfo <- saem(f_mmkin[\"SFO-SFO\", ]) f_saem_dfop_sfo <- saem(f_mmkin[\"DFOP-SFO\", ]) # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo) #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_A1 * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   839.2 834.1 -406.6 #>  #> Fitted parameters: #>                    estimate    lower   upper #> parent_0           93.70402 91.04104 96.3670 #> log_k_A1           -5.83760 -7.66452 -4.0107 #> f_parent_qlogis    -0.95718 -1.35955 -0.5548 #> log_k1             -2.35514 -3.39402 -1.3163 #> log_k2             -3.79634 -5.64009 -1.9526 #> g_qlogis           -0.02108 -0.66463  0.6225 #> a.1                 1.88191  1.66491  2.0989 #> SD.parent_0         2.81628  0.78922  4.8433 #> SD.log_k_A1         1.78751  0.42105  3.1540 #> SD.f_parent_qlogis  0.45016  0.16116  0.7391 #> SD.log_k1           1.06923  0.31676  1.8217 #> SD.log_k2           2.03768  0.70938  3.3660 #> SD.g_qlogis         0.44024 -0.09262  0.9731 plot(f_saem_dfop_sfo)  summary(f_saem_dfop_sfo, data = TRUE) #> saemix version used for fitting:      3.4  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.2  #> Date of fit:     Fri Nov 28 09:18:42 2025  #> Date of summary: Fri Nov 28 09:18:42 2025  #>  #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_A1 * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 3.833 s #> Using 300, 100 iterations and 10 chains #>  #> Variance model: Constant variance  #>  #> Starting values for degradation parameters: #>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2  #>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708  #>        g_qlogis  #>          0.1356  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis #> parent_0           4.941    0.000          0.0000  0.000  0.000   0.0000 #> log_k_A1           0.000    2.551          0.0000  0.000  0.000   0.0000 #> f_parent_qlogis    0.000    0.000          0.7251  0.000  0.000   0.0000 #> log_k1             0.000    0.000          0.0000  1.449  0.000   0.0000 #> log_k2             0.000    0.000          0.0000  0.000  2.228   0.0000 #> g_qlogis           0.000    0.000          0.0000  0.000  0.000   0.7814 #>  #> Starting values for error model parameters: #> a.1  #>   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   839.2 834.1 -406.6 #>  #> Optimised parameters: #>                        est.    lower   upper #> parent_0           93.70402 91.04104 96.3670 #> log_k_A1           -5.83760 -7.66452 -4.0107 #> f_parent_qlogis    -0.95718 -1.35955 -0.5548 #> log_k1             -2.35514 -3.39402 -1.3163 #> log_k2             -3.79634 -5.64009 -1.9526 #> g_qlogis           -0.02108 -0.66463  0.6225 #> a.1                 1.88191  1.66491  2.0989 #> SD.parent_0         2.81628  0.78922  4.8433 #> SD.log_k_A1         1.78751  0.42105  3.1540 #> SD.f_parent_qlogis  0.45016  0.16116  0.7391 #> SD.log_k1           1.06923  0.31676  1.8217 #> SD.log_k2           2.03768  0.70938  3.3660 #> SD.g_qlogis         0.44024 -0.09262  0.9731 #>  #> Correlation:  #>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2  #> log_k_A1        -0.0147                                 #> f_parent_qlogis -0.0269  0.0573                         #> log_k1           0.0263 -0.0011 -0.0040                 #> log_k2           0.0020  0.0065 -0.0002 -0.0776         #> g_qlogis        -0.0248 -0.0180 -0.0004 -0.0903 -0.0603 #>  #> Random effects: #>                      est.    lower  upper #> SD.parent_0        2.8163  0.78922 4.8433 #> SD.log_k_A1        1.7875  0.42105 3.1540 #> SD.f_parent_qlogis 0.4502  0.16116 0.7391 #> SD.log_k1          1.0692  0.31676 1.8217 #> SD.log_k2          2.0377  0.70938 3.3660 #> SD.g_qlogis        0.4402 -0.09262 0.9731 #>  #> Variance model: #>      est. lower upper #> a.1 1.882 1.665 2.099 #>  #> Backtransformed parameters: #>                     est.     lower    upper #> parent_0       93.704015 9.104e+01 96.36699 #> k_A1            0.002916 4.692e-04  0.01812 #> f_parent_to_A1  0.277443 2.043e-01  0.36475 #> k1              0.094880 3.357e-02  0.26813 #> k2              0.022453 3.553e-03  0.14191 #> g               0.494731 3.397e-01  0.65078 #>  #> Resulting formation fractions: #>                 ff #> parent_A1   0.2774 #> parent_sink 0.7226 #>  #> Estimated disappearance times: #>         DT50   DT90 DT50back DT50_k1 DT50_k2 #> parent  14.0  72.38    21.79   7.306   30.87 #> A1     237.7 789.68       NA      NA      NA #>  #> Data: #>          ds   name time observed predicted residual   std standardized #>   Dataset 6 parent    0     97.2  95.70025  1.49975 1.882      0.79693 #>   Dataset 6 parent    0     96.4  95.70025  0.69975 1.882      0.37183 #>   Dataset 6 parent    3     71.1  71.44670 -0.34670 1.882     -0.18423 #>   Dataset 6 parent    3     69.2  71.44670 -2.24670 1.882     -1.19384 #>   Dataset 6 parent    6     58.1  56.59283  1.50717 1.882      0.80087 #>   Dataset 6 parent    6     56.6  56.59283  0.00717 1.882      0.00381 #>   Dataset 6 parent   10     44.4  44.56648 -0.16648 1.882     -0.08847 #>   Dataset 6 parent   10     43.4  44.56648 -1.16648 1.882     -0.61984 #>   Dataset 6 parent   20     33.3  29.76020  3.53980 1.882      1.88096 #>   Dataset 6 parent   20     29.2  29.76020 -0.56020 1.882     -0.29767 #>   Dataset 6 parent   34     17.6  19.39208 -1.79208 1.882     -0.95226 #>   Dataset 6 parent   34     18.0  19.39208 -1.39208 1.882     -0.73971 #>   Dataset 6 parent   55     10.5  10.55761 -0.05761 1.882     -0.03061 #>   Dataset 6 parent   55      9.3  10.55761 -1.25761 1.882     -0.66826 #>   Dataset 6 parent   90      4.5   3.84742  0.65258 1.882      0.34676 #>   Dataset 6 parent   90      4.7   3.84742  0.85258 1.882      0.45304 #>   Dataset 6 parent  112      3.0   2.03997  0.96003 1.882      0.51013 #>   Dataset 6 parent  112      3.4   2.03997  1.36003 1.882      0.72268 #>   Dataset 6 parent  132      2.3   1.14585  1.15415 1.882      0.61328 #>   Dataset 6 parent  132      2.7   1.14585  1.55415 1.882      0.82583 #>   Dataset 6     A1    3      4.3   4.86054 -0.56054 1.882     -0.29786 #>   Dataset 6     A1    3      4.6   4.86054 -0.26054 1.882     -0.13844 #>   Dataset 6     A1    6      7.0   7.74179 -0.74179 1.882     -0.39417 #>   Dataset 6     A1    6      7.2   7.74179 -0.54179 1.882     -0.28789 #>   Dataset 6     A1   10      8.2   9.94048 -1.74048 1.882     -0.92485 #>   Dataset 6     A1   10      8.0   9.94048 -1.94048 1.882     -1.03112 #>   Dataset 6     A1   20     11.0  12.19109 -1.19109 1.882     -0.63291 #>   Dataset 6     A1   20     13.7  12.19109  1.50891 1.882      0.80180 #>   Dataset 6     A1   34     11.5  13.10706 -1.60706 1.882     -0.85395 #>   Dataset 6     A1   34     12.7  13.10706 -0.40706 1.882     -0.21630 #>   Dataset 6     A1   55     14.9  13.06131  1.83869 1.882      0.97703 #>   Dataset 6     A1   55     14.5  13.06131  1.43869 1.882      0.76448 #>   Dataset 6     A1   90     12.1  11.54495  0.55505 1.882      0.29494 #>   Dataset 6     A1   90     12.3  11.54495  0.75505 1.882      0.40122 #>   Dataset 6     A1  112      9.9  10.31533 -0.41533 1.882     -0.22070 #>   Dataset 6     A1  112     10.2  10.31533 -0.11533 1.882     -0.06128 #>   Dataset 6     A1  132      8.8   9.20222 -0.40222 1.882     -0.21373 #>   Dataset 6     A1  132      7.8   9.20222 -1.40222 1.882     -0.74510 #>   Dataset 7 parent    0     93.6  90.82357  2.77643 1.882      1.47532 #>   Dataset 7 parent    0     92.3  90.82357  1.47643 1.882      0.78453 #>   Dataset 7 parent    3     87.0  84.73448  2.26552 1.882      1.20384 #>   Dataset 7 parent    3     82.2  84.73448 -2.53448 1.882     -1.34675 #>   Dataset 7 parent    7     74.0  77.65013 -3.65013 1.882     -1.93958 #>   Dataset 7 parent    7     73.9  77.65013 -3.75013 1.882     -1.99272 #>   Dataset 7 parent   14     64.2  67.60639 -3.40639 1.882     -1.81007 #>   Dataset 7 parent   14     69.5  67.60639  1.89361 1.882      1.00621 #>   Dataset 7 parent   30     54.0  52.53663  1.46337 1.882      0.77760 #>   Dataset 7 parent   30     54.6  52.53663  2.06337 1.882      1.09642 #>   Dataset 7 parent   60     41.1  39.42728  1.67272 1.882      0.88884 #>   Dataset 7 parent   60     38.4  39.42728 -1.02728 1.882     -0.54587 #>   Dataset 7 parent   90     32.5  33.76360 -1.26360 1.882     -0.67144 #>   Dataset 7 parent   90     35.5  33.76360  1.73640 1.882      0.92268 #>   Dataset 7 parent  120     28.1  30.39975 -2.29975 1.882     -1.22203 #>   Dataset 7 parent  120     29.0  30.39975 -1.39975 1.882     -0.74379 #>   Dataset 7 parent  180     26.5  25.62379  0.87621 1.882      0.46559 #>   Dataset 7 parent  180     27.6  25.62379  1.97621 1.882      1.05010 #>   Dataset 7     A1    3      3.9   2.70005  1.19995 1.882      0.63762 #>   Dataset 7     A1    3      3.1   2.70005  0.39995 1.882      0.21252 #>   Dataset 7     A1    7      6.9   5.83475  1.06525 1.882      0.56605 #>   Dataset 7     A1    7      6.6   5.83475  0.76525 1.882      0.40663 #>   Dataset 7     A1   14     10.4  10.26142  0.13858 1.882      0.07364 #>   Dataset 7     A1   14      8.3  10.26142 -1.96142 1.882     -1.04225 #>   Dataset 7     A1   30     14.4  16.82999 -2.42999 1.882     -1.29123 #>   Dataset 7     A1   30     13.7  16.82999 -3.12999 1.882     -1.66319 #>   Dataset 7     A1   60     22.1  22.32486 -0.22486 1.882     -0.11949 #>   Dataset 7     A1   60     22.3  22.32486 -0.02486 1.882     -0.01321 #>   Dataset 7     A1   90     27.5  24.45927  3.04073 1.882      1.61576 #>   Dataset 7     A1   90     25.4  24.45927  0.94073 1.882      0.49988 #>   Dataset 7     A1  120     28.0  25.54862  2.45138 1.882      1.30260 #>   Dataset 7     A1  120     26.6  25.54862  1.05138 1.882      0.55868 #>   Dataset 7     A1  180     25.8  26.82277 -1.02277 1.882     -0.54347 #>   Dataset 7     A1  180     25.3  26.82277 -1.52277 1.882     -0.80916 #>   Dataset 8 parent    0     91.9  91.16791  0.73209 1.882      0.38901 #>   Dataset 8 parent    0     90.8  91.16791 -0.36791 1.882     -0.19550 #>   Dataset 8 parent    1     64.9  67.58358 -2.68358 1.882     -1.42598 #>   Dataset 8 parent    1     66.2  67.58358 -1.38358 1.882     -0.73520 #>   Dataset 8 parent    3     43.5  41.62086  1.87914 1.882      0.99853 #>   Dataset 8 parent    3     44.1  41.62086  2.47914 1.882      1.31735 #>   Dataset 8 parent    8     18.3  19.60116 -1.30116 1.882     -0.69140 #>   Dataset 8 parent    8     18.1  19.60116 -1.50116 1.882     -0.79768 #>   Dataset 8 parent   14     10.2  10.63101 -0.43101 1.882     -0.22903 #>   Dataset 8 parent   14     10.8  10.63101  0.16899 1.882      0.08980 #>   Dataset 8 parent   27      4.9   3.12435  1.77565 1.882      0.94354 #>   Dataset 8 parent   27      3.3   3.12435  0.17565 1.882      0.09334 #>   Dataset 8 parent   48      1.6   0.43578  1.16422 1.882      0.61864 #>   Dataset 8 parent   48      1.5   0.43578  1.06422 1.882      0.56550 #>   Dataset 8 parent   70      1.1   0.05534  1.04466 1.882      0.55510 #>   Dataset 8 parent   70      0.9   0.05534  0.84466 1.882      0.44883 #>   Dataset 8     A1    1      9.6   7.63450  1.96550 1.882      1.04442 #>   Dataset 8     A1    1      7.7   7.63450  0.06550 1.882      0.03481 #>   Dataset 8     A1    3     15.0  15.52593 -0.52593 1.882     -0.27947 #>   Dataset 8     A1    3     15.1  15.52593 -0.42593 1.882     -0.22633 #>   Dataset 8     A1    8     21.2  20.32192  0.87808 1.882      0.46659 #>   Dataset 8     A1    8     21.1  20.32192  0.77808 1.882      0.41345 #>   Dataset 8     A1   14     19.7  20.09721 -0.39721 1.882     -0.21107 #>   Dataset 8     A1   14     18.9  20.09721 -1.19721 1.882     -0.63617 #>   Dataset 8     A1   27     17.5  16.37477  1.12523 1.882      0.59792 #>   Dataset 8     A1   27     15.9  16.37477 -0.47477 1.882     -0.25228 #>   Dataset 8     A1   48      9.5  10.13141 -0.63141 1.882     -0.33551 #>   Dataset 8     A1   48      9.8  10.13141 -0.33141 1.882     -0.17610 #>   Dataset 8     A1   70      6.2   5.81827  0.38173 1.882      0.20284 #>   Dataset 8     A1   70      6.1   5.81827  0.28173 1.882      0.14970 #>   Dataset 9 parent    0     99.8  97.48728  2.31272 1.882      1.22892 #>   Dataset 9 parent    0     98.3  97.48728  0.81272 1.882      0.43186 #>   Dataset 9 parent    1     77.1  79.29476 -2.19476 1.882     -1.16624 #>   Dataset 9 parent    1     77.2  79.29476 -2.09476 1.882     -1.11310 #>   Dataset 9 parent    3     59.0  55.67060  3.32940 1.882      1.76915 #>   Dataset 9 parent    3     58.1  55.67060  2.42940 1.882      1.29092 #>   Dataset 9 parent    8     27.4  31.57871 -4.17871 1.882     -2.22046 #>   Dataset 9 parent    8     29.2  31.57871 -2.37871 1.882     -1.26398 #>   Dataset 9 parent   14     19.1  22.51546 -3.41546 1.882     -1.81489 #>   Dataset 9 parent   14     29.6  22.51546  7.08454 1.882      3.76454 #>   Dataset 9 parent   27     10.1  14.09074 -3.99074 1.882     -2.12057 #>   Dataset 9 parent   27     18.2  14.09074  4.10926 1.882      2.18355 #>   Dataset 9 parent   48      4.5   6.95747 -2.45747 1.882     -1.30584 #>   Dataset 9 parent   48      9.1   6.95747  2.14253 1.882      1.13848 #>   Dataset 9 parent   70      2.3   3.32472 -1.02472 1.882     -0.54451 #>   Dataset 9 parent   70      2.9   3.32472 -0.42472 1.882     -0.22569 #>   Dataset 9 parent   91      2.0   1.64300  0.35700 1.882      0.18970 #>   Dataset 9 parent   91      1.8   1.64300  0.15700 1.882      0.08343 #>   Dataset 9 parent  120      2.0   0.62073  1.37927 1.882      0.73291 #>   Dataset 9 parent  120      2.2   0.62073  1.57927 1.882      0.83918 #>   Dataset 9     A1    1      4.2   3.64568  0.55432 1.882      0.29455 #>   Dataset 9     A1    1      3.9   3.64568  0.25432 1.882      0.13514 #>   Dataset 9     A1    3      7.4   8.30173 -0.90173 1.882     -0.47916 #>   Dataset 9     A1    3      7.9   8.30173 -0.40173 1.882     -0.21347 #>   Dataset 9     A1    8     14.5  12.71589  1.78411 1.882      0.94803 #>   Dataset 9     A1    8     13.7  12.71589  0.98411 1.882      0.52293 #>   Dataset 9     A1   14     14.2  13.90452  0.29548 1.882      0.15701 #>   Dataset 9     A1   14     12.2  13.90452 -1.70452 1.882     -0.90574 #>   Dataset 9     A1   27     13.7  14.15523 -0.45523 1.882     -0.24190 #>   Dataset 9     A1   27     13.2  14.15523 -0.95523 1.882     -0.50759 #>   Dataset 9     A1   48     13.6  13.31038  0.28962 1.882      0.15389 #>   Dataset 9     A1   48     15.4  13.31038  2.08962 1.882      1.11037 #>   Dataset 9     A1   70     10.4  11.85965 -1.45965 1.882     -0.77562 #>   Dataset 9     A1   70     11.6  11.85965 -0.25965 1.882     -0.13797 #>   Dataset 9     A1   91     10.0  10.36294 -0.36294 1.882     -0.19286 #>   Dataset 9     A1   91      9.5  10.36294 -0.86294 1.882     -0.45855 #>   Dataset 9     A1  120      9.1   8.43003  0.66997 1.882      0.35601 #>   Dataset 9     A1  120      9.0   8.43003  0.56997 1.882      0.30287 #>  Dataset 10 parent    0     96.1  93.95603  2.14397 1.882      1.13925 #>  Dataset 10 parent    0     94.3  93.95603  0.34397 1.882      0.18278 #>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237 #>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237 #>  Dataset 10 parent   14     69.4  70.04570 -0.64570 1.882     -0.34311 #>  Dataset 10 parent   14     73.1  70.04570  3.05430 1.882      1.62298 #>  Dataset 10 parent   21     65.6  64.01710  1.58290 1.882      0.84111 #>  Dataset 10 parent   21     65.3  64.01710  1.28290 1.882      0.68170 #>  Dataset 10 parent   41     55.9  54.98434  0.91566 1.882      0.48656 #>  Dataset 10 parent   41     54.4  54.98434 -0.58434 1.882     -0.31050 #>  Dataset 10 parent   63     47.0  49.87137 -2.87137 1.882     -1.52577 #>  Dataset 10 parent   63     49.3  49.87137 -0.57137 1.882     -0.30361 #>  Dataset 10 parent   91     44.7  45.06727 -0.36727 1.882     -0.19516 #>  Dataset 10 parent   91     46.7  45.06727  1.63273 1.882      0.86759 #>  Dataset 10 parent  120     42.1  40.76402  1.33598 1.882      0.70991 #>  Dataset 10 parent  120     41.3  40.76402  0.53598 1.882      0.28481 #>  Dataset 10     A1    8      3.3   4.14599 -0.84599 1.882     -0.44954 #>  Dataset 10     A1    8      3.4   4.14599 -0.74599 1.882     -0.39640 #>  Dataset 10     A1   14      3.9   6.08478 -2.18478 1.882     -1.16093 #>  Dataset 10     A1   14      2.9   6.08478 -3.18478 1.882     -1.69231 #>  Dataset 10     A1   21      6.4   7.59411 -1.19411 1.882     -0.63452 #>  Dataset 10     A1   21      7.2   7.59411 -0.39411 1.882     -0.20942 #>  Dataset 10     A1   41      9.1   9.78292 -0.68292 1.882     -0.36289 #>  Dataset 10     A1   41      8.5   9.78292 -1.28292 1.882     -0.68171 #>  Dataset 10     A1   63     11.7  10.93274  0.76726 1.882      0.40770 #>  Dataset 10     A1   63     12.0  10.93274  1.06726 1.882      0.56711 #>  Dataset 10     A1   91     13.3  11.93986  1.36014 1.882      0.72274 #>  Dataset 10     A1   91     13.2  11.93986  1.26014 1.882      0.66961 #>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111 #>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791  # The following took about 6 minutes and currently fails with # \"Error in deSolve::lsoda(y = odeini, times = outtimes_deSolve, func = lsoda_func,  :  # illegal input detected before taking any integration steps - see written message\" #f_saem_dfop_sfo_deSolve <- saem(f_mmkin[\"DFOP-SFO\", ], solution_type = \"deSolve\", #  nbiter.saemix = c(200, 80))  #anova( #  f_saem_dfop_sfo, #  f_saem_dfop_sfo_deSolve))  # If the model supports it, we can also use eigenvalue based solutions, which # take a similar amount of time #f_saem_sfo_sfo_eigen <- saem(f_mmkin[\"SFO-SFO\", ], solution_type = \"eigen\", #  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"dataset used comparison KinGUI ModelMaker check   software quality KinGUI original publication (Schäfer et al., 2007).   results fitting also included.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"","code":"schaefer07_complex_case"},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"data set data frame 8 observations following 6 variables. time numeric vector parent numeric vector A1 numeric vector B1 numeric vector C1 numeric vector A2 numeric vector results data frame 14 results different parameter values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"Schäfer D, Mikolasch B, Rainbird P Harvey B (2007). KinGUI: new kinetic   software tool evaluations according FOCUS degradation kinetics. : Del   Re AAM, Capri E, Fragoulis G Trevisan M (Eds.). Proceedings XIII   Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"","code":"data <- mkin_wide_to_long(schaefer07_complex_case, time = \"time\") model <- mkinmod(   parent = list(type = \"SFO\", to = c(\"A1\", \"B1\", \"C1\"), sink = FALSE),   A1 = list(type = \"SFO\", to = \"A2\"),   B1 = list(type = \"SFO\"),   C1 = list(type = \"SFO\"),   A2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   # \\dontrun{     fit <- mkinfit(model, data, quiet = TRUE)     plot(fit)      endpoints(fit) #> $ff #>   parent_A1   parent_B1   parent_C1 parent_sink       A1_A2     A1_sink  #>   0.3809618   0.1954668   0.4235714   0.0000000   0.4479540   0.5520460  #>  #> $distimes #>            DT50      DT90 #> parent 13.95078  46.34349 #> A1     49.75347 165.27745 #> B1     37.26905 123.80511 #> C1     11.23129  37.30955 #> A2     28.50690  94.69789 #>    # }  # Compare with the results obtained in the original publication  print(schaefer07_complex_results) #>         compound          parameter  KinGUI ModelMaker deviation #> 1         parent   degradation rate  0.0496     0.0506       2.0 #> 2         parent               DT50 13.9900    13.6900       2.2 #> 3  metabolite A1 formation fraction  0.3803     0.3696       2.9 #> 4  metabolite A1   degradation rate  0.0139     0.0136       2.2 #> 5  metabolite A1               DT50 49.9600    50.8900       1.8 #> 6  metabolite B1 formation fraction  0.1866     0.1818       2.6 #> 7  metabolite B1   degradation rate  0.0175     0.0172       1.7 #> 8  metabolite B1               DT50 39.6100    40.2400       1.6 #> 9  metabolite C1 formation fraction  0.4331     0.4486       3.5 #> 10 metabolite C1   degradation rate  0.0638     0.0700       8.9 #> 11 metabolite C1               DT50 10.8700     9.9000       9.8 #> 12 metabolite A2 formation fraction  0.4529     0.4559       0.7 #> 13 metabolite A2   degradation rate  0.0245     0.0244       0.4 #> 14 metabolite A2               DT50 28.2400    28.4500       0.7"},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":null,"dir":"Reference","previous_headings":"","what":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"function automates replacing unquantified values residue time depth series. time series, function performs part residue processing proposed FOCUS kinetics guidance parent compounds metabolites. two-dimensional residue series time depth, automates proposal Boesten et al (2015).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"","code":"set_nd_nq(res_raw, lod, loq = NA, time_zero_presence = FALSE)  set_nd_nq_focus(   res_raw,   lod,   loq = NA,   set_first_sample_nd = TRUE,   first_sample_nd_value = 0,   ignore_below_loq_after_first_nd = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"res_raw Character vector residue time series, matrix residue values rows representing depth profiles specific sampling time, columns representing time series residues depth. Values limit detection (lod) coded \"nd\", values limit detection limit quantification, , coded \"nq\". Samples analysed coded \"na\". values \"na\", \"nd\" \"nq\" coercible numeric lod Limit detection (numeric) loq Limit quantification(numeric). Must specified FOCUS rule stop first non-detection applied time_zero_presence assume residues occur time zero? affects samples first sampling time reported \"nd\" (detected). set_first_sample_nd first sample set \"first_sample_nd_value\" case non-detection? first_sample_nd_value Value used first sample non-detection ignore_below_loq_after_first_nd ignore values LOQ first non-detection occurs quantified values?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"numeric vector, vector supplied, numeric matrix otherwise","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"set_nd_nq_focus(): Set non-detects residue time series according FOCUS rules","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"Boesten, J. J. T. ., van der Linden, . M. ., Beltman, W. H. J. Pol, J. W. (2015). Leaching plant protection products transformation products; Proposals improving assessment leaching groundwater Netherlands — Version 2. Alterra report 2630, Alterra Wageningen UR (University & Research centre) FOCUS (2014) Generic Guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration, Version 1.1, 18 December 2014, p. 251","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"","code":"# FOCUS (2014) p. 75/76 and 131/132 parent_1 <- c(.12, .09, .05, .03, \"nd\", \"nd\", \"nd\", \"nd\", \"nd\", \"nd\") set_nd_nq(parent_1, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA parent_2 <- c(.12, .09, .05, .03, \"nd\", \"nd\", .03, \"nd\", \"nd\", \"nd\") set_nd_nq(parent_2, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.03 0.01   NA   NA set_nd_nq_focus(parent_2, 0.02, loq = 0.05) #>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA parent_3 <- c(.12, .09, .05, .03, \"nd\", \"nd\", .06, \"nd\", \"nd\", \"nd\") set_nd_nq(parent_3, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA set_nd_nq_focus(parent_3, 0.02, loq = 0.05) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA metabolite <- c(\"nd\", \"nd\", \"nd\", 0.03, 0.06, 0.10, 0.11, 0.10, 0.09, 0.05, 0.03, \"nd\", \"nd\") set_nd_nq(metabolite, 0.02) #>  [1]   NA   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA set_nd_nq_focus(metabolite, 0.02, 0.05) #>  [1] 0.00   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA # # Boesten et al. (2015), p. 57/58 table_8 <- matrix(   c(10, 10, rep(\"nd\", 4),     10, 10, rep(\"nq\", 2), rep(\"nd\", 2),     10, 10, 10, \"nq\", \"nd\", \"nd\",     \"nq\", 10, \"nq\", rep(\"nd\", 3),     \"nd\", \"nq\", \"nq\", rep(\"nd\", 3),     rep(\"nd\", 6), rep(\"nd\", 6)),   ncol = 6, byrow = TRUE) set_nd_nq(table_8, 0.5, 1.5, time_zero_presence = TRUE) #>       [,1]  [,2]  [,3] [,4] [,5] [,6] #> [1,] 10.00 10.00  0.25 0.25   NA   NA #> [2,] 10.00 10.00  1.00 1.00 0.25   NA #> [3,] 10.00 10.00 10.00 1.00 0.25   NA #> [4,]  1.00 10.00  1.00 0.25   NA   NA #> [5,]  0.25  1.00  1.00 0.25   NA   NA #> [6,]    NA  0.25  0.25   NA   NA   NA #> [7,]    NA    NA    NA   NA   NA   NA table_10 <- matrix(   c(10, 10, rep(\"nd\", 4),     10, 10, rep(\"nd\", 4),     10, 10, 10, rep(\"nd\", 3),     \"nd\", 10, rep(\"nd\", 4),     rep(\"nd\", 18)),   ncol = 6, byrow = TRUE) set_nd_nq(table_10, 0.5, time_zero_presence = TRUE) #>       [,1]  [,2]  [,3] [,4] [,5] [,6] #> [1,] 10.00 10.00  0.25   NA   NA   NA #> [2,] 10.00 10.00  0.25   NA   NA   NA #> [3,] 10.00 10.00 10.00 0.25   NA   NA #> [4,]  0.25 10.00  0.25   NA   NA   NA #> [5,]    NA  0.25    NA   NA   NA   NA #> [6,]    NA    NA    NA   NA   NA   NA #> [7,]    NA    NA    NA   NA   NA   NA"},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Two-component error model — sigma_twocomp","title":"Two-component error model — sigma_twocomp","text":"Function describing standard deviation measurement error dependence measured value \\(y\\):","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Two-component error model — sigma_twocomp","text":"","code":"sigma_twocomp(y, sigma_low, rsd_high)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Two-component error model — sigma_twocomp","text":"y magnitude observed value sigma_low asymptotic minimum standard deviation low observed values rsd_high coefficient describing increase standard deviation magnitude observed value","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Two-component error model — sigma_twocomp","text":"standard deviation response variable.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Two-component error model — sigma_twocomp","text":"$$\\sigma = \\sqrt{ \\sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2) error model used example Werner et al. (1978). model proposed Rocke Lorenzato (1995) can written form well, assumes approximate lognormal distribution errors high values y.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Two-component error model — sigma_twocomp","text":"Werner, Mario, Brooks, Samuel H., Knott, Lancaster B. (1978) Additive, Multiplicative, Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898. Rocke, David M. Lorenzato, Stefan (1995) two-component model measurement error analytical chemistry. Technometrics 37(2), 176-184. Ranke J Meinecke S (2019) Error Models Kinetic Evaluation Chemical Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124 .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Two-component error model — sigma_twocomp","text":"","code":"times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120) d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times)) set.seed(123456) d_syn <- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),   reps = 2, n = 1)[[1]] f_nls <- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,  start = list(parent_0 = 100, lrc = -3)) library(nlme) f_gnls <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),   data = d_syn, na.action = na.omit,   start = list(parent_0 = 100, lrc = -3)) if (length(findFunction(\"varConstProp\")) > 0) {   f_gnls_tc <- update(f_gnls, weights = varConstProp())   f_gnls_tc_sf <- update(f_gnls_tc, control = list(sigma = 1)) } f_mkin <- mkinfit(\"SFO\", d_syn, error_model = \"const\", quiet = TRUE) f_mkin_tc <- mkinfit(\"SFO\", d_syn, error_model = \"tc\", quiet = TRUE) plot_res(f_mkin_tc, standardized = TRUE)  AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc) #>              df      AIC #> f_nls         3 114.4817 #> f_gnls        3 114.4817 #> f_gnls_tc     5 103.6447 #> f_gnls_tc_sf  4 101.6447 #> f_mkin        3 114.4817 #> f_mkin_tc     4 101.6446"},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get status information for fit array objects — status","title":"Method to get status information for fit array objects — status","text":"Method get status information fit array objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get status information for fit array objects — status","text":"","code":"status(object, ...)  # S3 method for class 'mmkin' status(object, ...)  # S3 method for class 'status.mmkin' print(x, ...)  # S3 method for class 'mhmkin' status(object, ...)  # S3 method for class 'status.mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get status information for fit array objects — status","text":"object object investigate ... potential future extensions x object printed","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get status information for fit array objects — status","text":"object dimensions fit array suitable printing method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Method to get status information for fit array objects — status","text":"","code":"# \\dontrun{ fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS B\" = FOCUS_2006_C),   quiet = TRUE) status(fits) #>       dataset #> model  FOCUS A FOCUS B #>   SFO  OK      OK      #>   FOMC C       OK      #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters uncertainty statistics, chi2 error levels calculated according FOCUS guidance (2006) defined therein, formation fractions, DT50 values optionally data, consisting observed, predicted residual values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'mkinfit' summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)  # S3 method for class 'summary.mkinfit' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class mkinfit. data logical, indicating whether data included summary. distimes logical, indicating whether DT50 DT90 values included. alpha error level confidence interval estimation t distribution ... optional arguments passed methods like print. x object class summary.mkinfit. digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list components, among others version, Rversion mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used model use_of_ff maximum minimum use made formation fractions bpar Optimised backtransformed parameters data data (see Description ). start starting values bounds, applicable, optimised parameters. fixed values fixed parameters. errmin chi2 error levels observed variable. bparms.ode backtransformed ODE parameters, use starting parameters related models. errparms Error model parameters. ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues fractional eigenvector component g SFORB systems model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary method for class ","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"summary(mkinfit(\"SFO\", FOCUS_2006_A, quiet = TRUE)) #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:26:43 2025  #> Date of summary: Mon May 12 20:26:43 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #>  #> Model predictions using solution type analytical  #>  #> Fitted using 131 model solutions performed in 0.01 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>           value   type #> parent_0 101.24  state #> k_parent   0.10 deparm #>  #> Starting values for the transformed parameters actually optimised: #>                   value lower upper #> parent_0     101.240000  -Inf   Inf #> log_k_parent  -2.302585  -Inf   Inf #>  #> Fixed parameter values: #> None #>  #> Results: #>  #>        AIC     BIC    logLik #>   55.28197 55.5203 -24.64099 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>              Estimate Std. Error  Lower   Upper #> parent_0      109.200    3.70400 99.630 118.700 #> log_k_parent   -3.291    0.09176 -3.527  -3.055 #> sigma           5.266    1.31600  1.882   8.649 #>  #> Parameter correlation: #>               parent_0 log_k_parent     sigma #> parent_0     1.000e+00    5.428e-01 1.642e-07 #> log_k_parent 5.428e-01    1.000e+00 2.507e-07 #> sigma        1.642e-07    2.507e-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_0 109.20000   29.47 4.218e-07 99.6300 118.70000 #> k_parent   0.03722   10.90 5.650e-05  0.0294   0.04712 #> sigma      5.26600    4.00 5.162e-03  1.8820   8.64900 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   8.385       2  6 #> parent     8.385       2  6 #>  #> Estimated disappearance times: #>         DT50  DT90 #> parent 18.62 61.87 #>  #> Data: #>  time variable observed predicted residual #>     0   parent   101.24   109.153  -7.9132 #>     3   parent    99.27    97.622   1.6484 #>     7   parent    90.11    84.119   5.9913 #>    14   parent    72.19    64.826   7.3641 #>    30   parent    29.71    35.738  -6.0283 #>    62   parent     5.98    10.862  -4.8818 #>    90   parent     1.54     3.831  -2.2911 #>   118   parent     0.39     1.351  -0.9613"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Shows status information mkinfit objects contained object gives overview ill-defined parameters calculated illparms.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'mmkin' summary(object, conf.level = 0.95, ...)  # S3 method for class 'summary.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class mmkin conf.level confidence level testing parameters ... optional arguments passed methods like print. x object class summary.mmkin. digits number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS C\" = FOCUS_2006_C),   quiet = TRUE, cores = 1) #> Warning: Optimisation did not converge: #> false convergence (8)   summary(fits) #> Error model: Constant variance  #> Fitted in 0.533 s #>  #> Status: #>       dataset #> model  FOCUS A FOCUS C #>   SFO  OK      OK      #>   FOMC C       OK      #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings #>  #> Ill-defined parameters: #>       dataset #> model  FOCUS A                      FOCUS C #>   SFO                                       #>   FOMC parent_0, alpha, beta, sigma"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters fixed effects (population), random effects (deviations population mean) residual error model, well resulting endpoints formation fractions DT50 values. Optionally (default FALSE), data listed full.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'nlme.mmkin' summary(   object,   data = FALSE,   verbose = FALSE,   distimes = TRUE,   alpha = 0.05,   ... )  # S3 method for class 'summary.nlme.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), verbose = x$verbose, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class nlme.mmkin data logical, indicating whether full data included summary. verbose summary verbose? distimes logical, indicating whether DT50 DT90 values included. alpha error level confidence interval estimation t distribution ... optional arguments passed methods like print. x object class summary.nlme.mmkin digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list based nlme object obtained fit, least following additional components nlmeversion, mkinversion, Rversion nlme, mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used degradation model use_of_ff maximum minimum use made formation fractions data data confint_trans Transformed parameters used optimisation, confidence intervals confint_back Backtransformed parameters, confidence intervals available ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues SFORB components model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke mkin specific parts José Pinheiro Douglas Bates components inherited nlme","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"# Generate five datasets following SFO kinetics sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) dt50_sfo_in_pop <- 50 k_in_pop <- log(2) / dt50_sfo_in_pop set.seed(1234) k_in <- rlnorm(5, log(k_in_pop), 0.5) SFO <- mkinmod(parent = mkinsub(\"SFO\"))  pred_sfo <- function(k) {   mkinpredict(SFO,     c(k_parent = k),     c(parent = 100),     sampling_times) }  ds_sfo_mean <- lapply(k_in, pred_sfo) names(ds_sfo_mean) <- paste(\"ds\", 1:5)  set.seed(12345) ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),     n = 1)[[1]] })  # \\dontrun{ # Evaluate using mmkin and nlme library(nlme) f_mmkin <- mmkin(\"SFO\", ds_sfo_syn, quiet = TRUE, error_model = \"tc\", cores = 1) #> Warning: Optimisation did not converge: #> iteration limit reached without convergence (10) f_nlme <- nlme(f_mmkin) summary(f_nlme, data = TRUE) #> nlme version used for fitting:      3.1.168  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.0  #> Date of fit:     Mon May 12 20:26:45 2025  #> Date of summary: Mon May 12 20:26:45 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 0.193 s using 4 iterations #>  #> Variance model: Two-component variance function  #>  #> Mean of starting values for individual parameters: #>     parent_0 log_k_parent  #>      101.569       -4.454  #>  #> Fixed degradation parameter values: #> None #>  #> Results: #>  #>     AIC   BIC logLik #>   584.5 599.5 -286.2 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>               lower    est.   upper #> parent_0     99.371 101.592 103.814 #> log_k_parent -4.973  -4.449  -3.926 #>  #> Correlation:  #>              parnt_0 #> log_k_parent 0.0507  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1) #>  Level: ds #>  Structure: Diagonal #>          parent_0 log_k_parent Residual #> StdDev: 6.921e-05       0.5863        1 #>  #> Variance function: #>  Structure: Constant plus proportion of variance covariate #>  Formula: ~fitted(.)  #>  Parameter estimates: #>        const         prop  #> 0.0001208313 0.0789967985  #>  #> Backtransformed parameters with asymmetric confidence intervals: #>              lower      est.     upper #> parent_0 99.370882 101.59243 103.81398 #> k_parent  0.006923   0.01168   0.01972 #>  #> Estimated disappearance times: #>         DT50  DT90 #> parent 59.32 197.1 #>  #> Data: #>    ds   name time observed predicted  residual    std standardized #>  ds 1 parent    0    104.1   101.592   2.50757 8.0255     0.312451 #>  ds 1 parent    0    105.0   101.592   3.40757 8.0255     0.424594 #>  ds 1 parent    1     98.5   100.796  -2.29571 7.9625    -0.288313 #>  ds 1 parent    1     96.1   100.796  -4.69571 7.9625    -0.589725 #>  ds 1 parent    3    101.9    99.221   2.67904 7.8381     0.341796 #>  ds 1 parent    3     85.2    99.221 -14.02096 7.8381    -1.788812 #>  ds 1 parent    7     99.1    96.145   2.95512 7.5951     0.389081 #>  ds 1 parent    7     93.0    96.145  -3.14488 7.5951    -0.414065 #>  ds 1 parent   14     88.1    90.989  -2.88944 7.1879    -0.401987 #>  ds 1 parent   14     84.1    90.989  -6.88944 7.1879    -0.958480 #>  ds 1 parent   28     80.2    81.493  -1.29305 6.4377    -0.200857 #>  ds 1 parent   28     91.3    81.493   9.80695 6.4377     1.523364 #>  ds 1 parent   60     65.1    63.344   1.75642 5.0039     0.351008 #>  ds 1 parent   60     65.8    63.344   2.45642 5.0039     0.490898 #>  ds 1 parent   90     47.8    50.018  -2.21764 3.9512    -0.561252 #>  ds 1 parent   90     53.5    50.018   3.48236 3.9512     0.881335 #>  ds 1 parent  120     37.6    39.495  -1.89515 3.1200    -0.607423 #>  ds 1 parent  120     39.3    39.495  -0.19515 3.1200    -0.062549 #>  ds 2 parent    0    107.9   101.592   6.30757 8.0255     0.785943 #>  ds 2 parent    0    102.1   101.592   0.50757 8.0255     0.063245 #>  ds 2 parent    1    103.8   100.058   3.74159 7.9043     0.473361 #>  ds 2 parent    1    108.6   100.058   8.54159 7.9043     1.080626 #>  ds 2 parent    3     91.0    97.060  -6.05952 7.6674    -0.790297 #>  ds 2 parent    3     84.9    97.060 -12.15952 7.6674    -1.585874 #>  ds 2 parent    7     79.3    91.329 -12.02867 7.2147    -1.667251 #>  ds 2 parent    7    100.9    91.329   9.57133 7.2147     1.326647 #>  ds 2 parent   14     77.3    82.102  -4.80185 6.4858    -0.740366 #>  ds 2 parent   14     83.5    82.102   1.39815 6.4858     0.215571 #>  ds 2 parent   28     66.8    66.351   0.44945 5.2415     0.085748 #>  ds 2 parent   28     63.3    66.351  -3.05055 5.2415    -0.582002 #>  ds 2 parent   60     40.8    40.775   0.02474 3.2211     0.007679 #>  ds 2 parent   60     44.8    40.775   4.02474 3.2211     1.249485 #>  ds 2 parent   90     27.8    25.832   1.96762 2.0407     0.964198 #>  ds 2 parent   90     27.0    25.832   1.16762 2.0407     0.572171 #>  ds 2 parent  120     15.2    16.366  -1.16561 1.2928    -0.901595 #>  ds 2 parent  120     15.5    16.366  -0.86561 1.2928    -0.669547 #>  ds 3 parent    0     97.7   101.592  -3.89243 8.0255    -0.485009 #>  ds 3 parent    0     88.2   101.592 -13.39243 8.0255    -1.668740 #>  ds 3 parent    1    109.9    99.218  10.68196 7.8379     1.362858 #>  ds 3 parent    1     97.8    99.218  -1.41804 7.8379    -0.180921 #>  ds 3 parent    3    100.5    94.634   5.86555 7.4758     0.784603 #>  ds 3 parent    3     77.4    94.634 -17.23445 7.4758    -2.305360 #>  ds 3 parent    7     78.3    86.093  -7.79273 6.8011    -1.145813 #>  ds 3 parent    7     90.3    86.093   4.20727 6.8011     0.618620 #>  ds 3 parent   14     76.0    72.958   3.04222 5.7634     0.527848 #>  ds 3 parent   14     79.1    72.958   6.14222 5.7634     1.065722 #>  ds 3 parent   28     46.0    52.394  -6.39404 4.1390    -1.544842 #>  ds 3 parent   28     53.4    52.394   1.00596 4.1390     0.243046 #>  ds 3 parent   60     25.1    24.582   0.51786 1.9419     0.266676 #>  ds 3 parent   60     21.4    24.582  -3.18214 1.9419    -1.638664 #>  ds 3 parent   90     11.0    12.092  -1.09202 0.9552    -1.143199 #>  ds 3 parent   90     14.2    12.092   2.10798 0.9552     2.206777 #>  ds 3 parent  120      5.8     5.948  -0.14810 0.4699    -0.315178 #>  ds 3 parent  120      6.1     5.948   0.15190 0.4699     0.323282 #>  ds 4 parent    0     95.3   101.592  -6.29243 8.0255    -0.784057 #>  ds 4 parent    0    102.0   101.592   0.40757 8.0255     0.050784 #>  ds 4 parent    1    104.4   101.125   3.27549 7.9885     0.410025 #>  ds 4 parent    1    105.4   101.125   4.27549 7.9885     0.535205 #>  ds 4 parent    3    113.7   100.195  13.50487 7.9151     1.706218 #>  ds 4 parent    3     82.3   100.195 -17.89513 7.9151    -2.260886 #>  ds 4 parent    7     98.1    98.362  -0.26190 7.7703    -0.033706 #>  ds 4 parent    7     87.8    98.362 -10.56190 7.7703    -1.359270 #>  ds 4 parent   14     97.9    95.234   2.66590 7.5232     0.354357 #>  ds 4 parent   14    104.8    95.234   9.56590 7.5232     1.271521 #>  ds 4 parent   28     85.0    89.274  -4.27372 7.0523    -0.606001 #>  ds 4 parent   28     77.2    89.274 -12.07372 7.0523    -1.712017 #>  ds 4 parent   60     82.2    77.013   5.18661 6.0838     0.852526 #>  ds 4 parent   60     86.1    77.013   9.08661 6.0838     1.493571 #>  ds 4 parent   90     70.5    67.053   3.44692 5.2970     0.650733 #>  ds 4 parent   90     61.7    67.053  -5.35308 5.2970    -1.010591 #>  ds 4 parent  120     60.0    58.381   1.61905 4.6119     0.351058 #>  ds 4 parent  120     56.4    58.381  -1.98095 4.6119    -0.429530 #>  ds 5 parent    0     92.6   101.592  -8.99243 8.0255    -1.120485 #>  ds 5 parent    0    116.5   101.592  14.90757 8.0255     1.857531 #>  ds 5 parent    1    108.0    99.914   8.08560 7.8929     1.024413 #>  ds 5 parent    1    104.9    99.914   4.98560 7.8929     0.631655 #>  ds 5 parent    3    100.5    96.641   3.85898 7.6343     0.505477 #>  ds 5 parent    3     89.5    96.641  -7.14102 7.6343    -0.935383 #>  ds 5 parent    7     91.7    90.412   1.28752 7.1423     0.180267 #>  ds 5 parent    7     95.1    90.412   4.68752 7.1423     0.656304 #>  ds 5 parent   14     82.2    80.463   1.73715 6.3563     0.273295 #>  ds 5 parent   14     84.5    80.463   4.03715 6.3563     0.635141 #>  ds 5 parent   28     60.5    63.728  -3.22788 5.0343    -0.641178 #>  ds 5 parent   28     72.8    63.728   9.07212 5.0343     1.802062 #>  ds 5 parent   60     38.3    37.399   0.90061 2.9544     0.304835 #>  ds 5 parent   60     40.7    37.399   3.30061 2.9544     1.117174 #>  ds 5 parent   90     22.5    22.692  -0.19165 1.7926    -0.106913 #>  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273 #>  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259 #>  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters fixed effects (population), random effects (deviations population mean) residual error model, well resulting endpoints formation fractions DT50 values. Optionally (default FALSE), data listed full.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'saem.mmkin' summary(   object,   data = FALSE,   verbose = FALSE,   covariates = NULL,   covariate_quantile = 0.5,   distimes = TRUE,   ... )  # S3 method for class 'summary.saem.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), verbose = x$verbose, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class saem.mmkin data logical, indicating whether full data included summary. verbose summary verbose? covariates Numeric vector covariate values variables covariate models object. given, overrides 'covariate_quantile'. covariate_quantile argument effect fitted object covariate models. , default show endpoints median covariate values (50th percentile). distimes logical, indicating whether DT50 DT90 values included. ... optional arguments passed methods like print. x object class summary.saem.mmkin digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list based saemix::SaemixObject obtained fit, least following additional components saemixversion, mkinversion, Rversion saemix, mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used degradation model use_of_ff maximum minimum use made formation fractions data data confint_trans Transformed parameters used optimisation, confidence intervals confint_back Backtransformed parameters, confidence intervals available confint_errmod Error model parameters confidence intervals ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues SFORB components model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke mkin specific parts saemix authors parts inherited saemix.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"# Generate five datasets following DFOP-SFO kinetics sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"m1\"),  m1 = mkinsub(\"SFO\"), quiet = TRUE) set.seed(1234) k1_in <- rlnorm(5, log(0.1), 0.3) k2_in <- rlnorm(5, log(0.02), 0.3) g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) k_m1_in <- rlnorm(5, log(0.02), 0.3)  pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {   mkinpredict(dfop_sfo,     c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),     c(parent = 100, m1 = 0),     sampling_times) }  ds_mean_dfop_sfo <- lapply(1:5, function(i) {   mkinpredict(dfop_sfo,     c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],       f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),     c(parent = 100, m1 = 0),     sampling_times) }) names(ds_mean_dfop_sfo) <- paste(\"ds\", 1:5)  ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),     n = 1)[[1]] })  # \\dontrun{ # Evaluate using mmkin and saem f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,   quiet = TRUE, error_model = \"tc\", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) print(f_saem_dfop_sfo) #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 171 observations of 2 variable(s) grouped in 5 datasets #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   810.8 805.4 -391.4 #>  #> Fitted parameters: #>                      estimate     lower     upper #> parent_0           100.966822  97.90584 104.02780 #> log_k_m1            -4.076164  -4.17485  -3.97748 #> f_parent_qlogis     -0.940902  -1.35358  -0.52823 #> log_k1              -2.363988  -2.71690  -2.01107 #> log_k2              -4.060016  -4.21743  -3.90260 #> g_qlogis            -0.029999  -0.44766   0.38766 #> a.1                  0.876272   0.67790   1.07464 #> b.1                  0.079594   0.06521   0.09398 #> SD.parent_0          0.076322 -76.45825  76.61089 #> SD.log_k_m1          0.005052  -1.08943   1.09953 #> SD.f_parent_qlogis   0.446968   0.16577   0.72816 #> SD.log_k1            0.348786   0.09502   0.60255 #> SD.log_k2            0.147456   0.03111   0.26380 #> SD.g_qlogis          0.348244   0.02794   0.66854 illparms(f_saem_dfop_sfo) #> [1] \"sd(parent_0)\" \"sd(log_k_m1)\" f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo,   no_random_effect = c(\"parent_0\", \"log_k_m1\")) illparms(f_saem_dfop_sfo_2) intervals(f_saem_dfop_sfo_2) #> Approximate 95% confidence intervals #>  #>  Fixed effects: #>                      lower         est.        upper #> parent_0       98.04247057 101.09950884 104.15654711 #> k_m1            0.01528983   0.01687734   0.01862969 #> f_parent_to_m1  0.20447650   0.27932896   0.36887691 #> k1              0.06779844   0.09638524   0.13702550 #> k2              0.01495629   0.01741775   0.02028431 #> g               0.37669311   0.48368409   0.59219202 #>  #>  Random effects: #>                          lower      est.     upper #> sd(f_parent_qlogis) 0.16515113 0.4448330 0.7245148 #> sd(log_k1)          0.08982399 0.3447403 0.5996565 #> sd(log_k2)          0.02806780 0.1419560 0.2558443 #> sd(g_qlogis)        0.04908644 0.3801993 0.7113121 #>  #>   #>          lower       est.      upper #> a.1 0.67993373 0.87630147 1.07266921 #> b.1 0.06522297 0.07920531 0.09318766 summary(f_saem_dfop_sfo_2, data = TRUE) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.10  #> R version used for fitting:         4.5.0  #> Date of fit:     Mon May 12 20:27:02 2025  #> Date of summary: Mon May 12 20:27:02 2025  #>  #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 171 observations of 2 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 9.206 s #> Using 300, 100 iterations and 10 chains #>  #> Variance model: Two-component variance function  #>  #> Starting values for degradation parameters: #>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2  #>       101.65645        -4.05368        -0.94311        -2.35943        -4.07006  #>        g_qlogis  #>        -0.01133  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>                 parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis #> parent_0           6.742   0.0000          0.0000 0.0000 0.0000    0.000 #> log_k_m1           0.000   0.2236          0.0000 0.0000 0.0000    0.000 #> f_parent_qlogis    0.000   0.0000          0.5572 0.0000 0.0000    0.000 #> log_k1             0.000   0.0000          0.0000 0.8031 0.0000    0.000 #> log_k2             0.000   0.0000          0.0000 0.0000 0.2931    0.000 #> g_qlogis           0.000   0.0000          0.0000 0.0000 0.0000    0.807 #>  #> Starting values for error model parameters: #> a.1 b.1  #>   1   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   806.9 802.2 -391.5 #>  #> Optimised parameters: #>                         est.    lower     upper #> parent_0           101.09951 98.04247 104.15655 #> log_k_m1            -4.08178 -4.18057  -3.98300 #> f_parent_qlogis     -0.94779 -1.35855  -0.53704 #> log_k1              -2.33940 -2.69122  -1.98759 #> log_k2              -4.05027 -4.20262  -3.89791 #> g_qlogis            -0.06529 -0.50361   0.37303 #> a.1                  0.87630  0.67993   1.07267 #> b.1                  0.07921  0.06522   0.09319 #> SD.f_parent_qlogis   0.44483  0.16515   0.72451 #> SD.log_k1            0.34474  0.08982   0.59966 #> SD.log_k2            0.14196  0.02807   0.25584 #> SD.g_qlogis          0.38020  0.04909   0.71131 #>  #> Correlation:  #>                 parnt_0 lg_k_m1 f_prnt_ log_k1  log_k2  #> log_k_m1        -0.4716                                 #> f_parent_qlogis -0.2394  0.2617                         #> log_k1           0.1677 -0.1566 -0.0659                 #> log_k2           0.0165  0.0638  0.0045  0.2013         #> g_qlogis         0.1118 -0.1118 -0.0340 -0.2324 -0.3419 #>  #> Random effects: #>                      est.   lower  upper #> SD.f_parent_qlogis 0.4448 0.16515 0.7245 #> SD.log_k1          0.3447 0.08982 0.5997 #> SD.log_k2          0.1420 0.02807 0.2558 #> SD.g_qlogis        0.3802 0.04909 0.7113 #>  #> Variance model: #>        est.   lower   upper #> a.1 0.87630 0.67993 1.07267 #> b.1 0.07921 0.06522 0.09319 #>  #> Backtransformed parameters: #>                     est.    lower     upper #> parent_0       101.09951 98.04247 104.15655 #> k_m1             0.01688  0.01529   0.01863 #> f_parent_to_m1   0.27933  0.20448   0.36888 #> k1               0.09639  0.06780   0.13703 #> k2               0.01742  0.01496   0.02028 #> g                0.48368  0.37669   0.59219 #>  #> Resulting formation fractions: #>                 ff #> parent_m1   0.2793 #> parent_sink 0.7207 #>  #> Estimated disappearance times: #>         DT50   DT90 DT50back DT50_k1 DT50_k2 #> parent 15.66  94.28    28.38   7.191    39.8 #> m1     41.07 136.43       NA      NA      NA #>  #> Data: #>    ds   name time observed  predicted  residual    std standardized #>  ds 1 parent    0     89.8  1.011e+02 -11.29951 8.0554    -1.402721 #>  ds 1 parent    0    104.1  1.011e+02   3.00049 8.0554     0.372481 #>  ds 1 parent    1     88.7  9.624e+01  -7.53600 7.6726    -0.982195 #>  ds 1 parent    1     95.5  9.624e+01  -0.73600 7.6726    -0.095925 #>  ds 1 parent    3     81.8  8.736e+01  -5.55672 6.9744    -0.796732 #>  ds 1 parent    3     94.5  8.736e+01   7.14328 6.9744     1.024217 #>  ds 1 parent    7     71.5  7.251e+01  -1.00511 5.8093    -0.173019 #>  ds 1 parent    7     70.3  7.251e+01  -2.20511 5.8093    -0.379585 #>  ds 1 parent   14     54.2  5.356e+01   0.63921 4.3319     0.147560 #>  ds 1 parent   14     49.6  5.356e+01  -3.96079 4.3319    -0.914340 #>  ds 1 parent   28     31.5  3.175e+01  -0.25429 2.6634    -0.095475 #>  ds 1 parent   28     28.8  3.175e+01  -2.95429 2.6634    -1.109218 #>  ds 1 parent   60     12.1  1.281e+01  -0.71388 1.3409    -0.532390 #>  ds 1 parent   60     13.6  1.281e+01   0.78612 1.3409     0.586271 #>  ds 1 parent   90      6.2  6.405e+00  -0.20462 1.0125    -0.202083 #>  ds 1 parent   90      8.3  6.405e+00   1.89538 1.0125     1.871910 #>  ds 1 parent  120      2.2  3.329e+00  -1.12941 0.9151    -1.234165 #>  ds 1 parent  120      2.4  3.329e+00  -0.92941 0.9151    -1.015615 #>  ds 1     m1    1      0.3  1.177e+00  -0.87699 0.8812    -0.995168 #>  ds 1     m1    1      0.2  1.177e+00  -0.97699 0.8812    -1.108644 #>  ds 1     m1    3      2.2  3.268e+00  -1.06821 0.9137    -1.169063 #>  ds 1     m1    3      3.0  3.268e+00  -0.26821 0.9137    -0.293536 #>  ds 1     m1    7      6.5  6.555e+00  -0.05539 1.0186    -0.054377 #>  ds 1     m1    7      5.0  6.555e+00  -1.55539 1.0186    -1.527022 #>  ds 1     m1   14     10.2  1.017e+01   0.03108 1.1902     0.026117 #>  ds 1     m1   14      9.5  1.017e+01  -0.66892 1.1902    -0.562010 #>  ds 1     m1   28     12.2  1.270e+01  -0.50262 1.3342    -0.376708 #>  ds 1     m1   28     13.4  1.270e+01   0.69738 1.3342     0.522686 #>  ds 1     m1   60     11.8  1.078e+01   1.01734 1.2236     0.831403 #>  ds 1     m1   60     13.2  1.078e+01   2.41734 1.2236     1.975530 #>  ds 1     m1   90      6.6  7.686e+00  -1.08586 1.0670    -1.017675 #>  ds 1     m1   90      9.3  7.686e+00   1.61414 1.0670     1.512779 #>  ds 1     m1  120      3.5  5.205e+00  -1.70467 0.9684    -1.760250 #>  ds 1     m1  120      5.4  5.205e+00   0.19533 0.9684     0.201701 #>  ds 2 parent    0    118.0  1.011e+02  16.90049 8.0554     2.098026 #>  ds 2 parent    0     99.8  1.011e+02  -1.29951 8.0554    -0.161321 #>  ds 2 parent    1     90.2  9.574e+01  -5.53784 7.6334    -0.725473 #>  ds 2 parent    1     94.6  9.574e+01  -1.13784 7.6334    -0.149060 #>  ds 2 parent    3     96.1  8.638e+01   9.72233 6.8975     1.409551 #>  ds 2 parent    3     78.4  8.638e+01  -7.97767 6.8975    -1.156610 #>  ds 2 parent    7     77.9  7.194e+01   5.95854 5.7651     1.033547 #>  ds 2 parent    7     77.7  7.194e+01   5.75854 5.7651     0.998856 #>  ds 2 parent   14     56.0  5.558e+01   0.42141 4.4885     0.093888 #>  ds 2 parent   14     54.7  5.558e+01  -0.87859 4.4885    -0.195742 #>  ds 2 parent   28     36.6  3.852e+01  -1.92382 3.1746    -0.605999 #>  ds 2 parent   28     36.8  3.852e+01  -1.72382 3.1746    -0.543000 #>  ds 2 parent   60     22.1  2.108e+01   1.02043 1.8856     0.541168 #>  ds 2 parent   60     24.7  2.108e+01   3.62043 1.8856     1.920034 #>  ds 2 parent   90     12.4  1.250e+01  -0.09675 1.3220    -0.073184 #>  ds 2 parent   90     10.8  1.250e+01  -1.69675 1.3220    -1.283492 #>  ds 2 parent  120      6.8  7.426e+00  -0.62587 1.0554    -0.593027 #>  ds 2 parent  120      7.9  7.426e+00   0.47413 1.0554     0.449242 #>  ds 2     m1    1      1.3  1.417e+00  -0.11735 0.8835    -0.132825 #>  ds 2     m1    3      3.7  3.823e+00  -0.12301 0.9271    -0.132673 #>  ds 2     m1    3      4.7  3.823e+00   0.87699 0.9271     0.945909 #>  ds 2     m1    7      8.1  7.288e+00   0.81180 1.0494     0.773619 #>  ds 2     m1    7      7.9  7.288e+00   0.61180 1.0494     0.583025 #>  ds 2     m1   14     10.1  1.057e+01  -0.46957 1.2119    -0.387459 #>  ds 2     m1   14     10.3  1.057e+01  -0.26957 1.2119    -0.222432 #>  ds 2     m1   28     10.7  1.234e+01  -1.63555 1.3124    -1.246185 #>  ds 2     m1   28     12.2  1.234e+01  -0.13555 1.3124    -0.103281 #>  ds 2     m1   60     10.7  1.065e+01   0.04641 1.2165     0.038151 #>  ds 2     m1   60     12.5  1.065e+01   1.84641 1.2165     1.517773 #>  ds 2     m1   90      9.1  8.177e+00   0.92337 1.0896     0.847403 #>  ds 2     m1   90      7.4  8.177e+00  -0.77663 1.0896    -0.712734 #>  ds 2     m1  120      6.1  5.966e+00   0.13404 0.9956     0.134631 #>  ds 2     m1  120      4.5  5.966e+00  -1.46596 0.9956    -1.472460 #>  ds 3 parent    0    106.2  1.011e+02   5.10049 8.0554     0.633175 #>  ds 3 parent    0    106.9  1.011e+02   5.80049 8.0554     0.720073 #>  ds 3 parent    1    107.4  9.365e+01  13.74627 7.4695     1.840332 #>  ds 3 parent    1     96.1  9.365e+01   2.44627 7.4695     0.327504 #>  ds 3 parent    3     79.4  8.139e+01  -1.99118 6.5059    -0.306058 #>  ds 3 parent    3     82.6  8.139e+01   1.20882 6.5059     0.185803 #>  ds 3 parent    7     63.9  6.445e+01  -0.54666 5.1792    -0.105549 #>  ds 3 parent    7     62.4  6.445e+01  -2.04666 5.1792    -0.395170 #>  ds 3 parent   14     51.0  4.830e+01   2.69944 3.9247     0.687800 #>  ds 3 parent   14     47.1  4.830e+01  -1.20056 3.9247    -0.305896 #>  ds 3 parent   28     36.1  3.426e+01   1.83885 2.8516     0.644839 #>  ds 3 parent   28     36.6  3.426e+01   2.33885 2.8516     0.820177 #>  ds 3 parent   60     20.1  1.968e+01   0.42208 1.7881     0.236053 #>  ds 3 parent   60     19.8  1.968e+01   0.12208 1.7881     0.068273 #>  ds 3 parent   90     11.3  1.194e+01  -0.64013 1.2893    -0.496496 #>  ds 3 parent   90     10.7  1.194e+01  -1.24013 1.2893    -0.961865 #>  ds 3 parent  120      8.2  7.247e+00   0.95264 1.0476     0.909381 #>  ds 3 parent  120      7.3  7.247e+00   0.05264 1.0476     0.050254 #>  ds 3     m1    0      0.8 -2.956e-12   0.80000 0.8763     0.912928 #>  ds 3     m1    1      1.8  1.757e+00   0.04318 0.8873     0.048666 #>  ds 3     m1    1      2.3  1.757e+00   0.54318 0.8873     0.612186 #>  ds 3     m1    3      4.2  4.566e+00  -0.36607 0.9480    -0.386149 #>  ds 3     m1    3      4.1  4.566e+00  -0.46607 0.9480    -0.491634 #>  ds 3     m1    7      6.8  8.157e+00  -1.35680 1.0887    -1.246241 #>  ds 3     m1    7     10.1  8.157e+00   1.94320 1.0887     1.784855 #>  ds 3     m1   14     11.4  1.085e+01   0.55367 1.2272     0.451182 #>  ds 3     m1   14     12.8  1.085e+01   1.95367 1.2272     1.592023 #>  ds 3     m1   28     11.5  1.149e+01   0.01098 1.2633     0.008689 #>  ds 3     m1   28     10.6  1.149e+01  -0.88902 1.2633    -0.703717 #>  ds 3     m1   60      7.5  9.295e+00  -1.79500 1.1445    -1.568351 #>  ds 3     m1   60      8.6  9.295e+00  -0.69500 1.1445    -0.607245 #>  ds 3     m1   90      7.3  7.017e+00   0.28305 1.0377     0.272775 #>  ds 3     m1   90      8.1  7.017e+00   1.08305 1.0377     1.043720 #>  ds 3     m1  120      5.3  5.087e+00   0.21272 0.9645     0.220547 #>  ds 3     m1  120      3.8  5.087e+00  -1.28728 0.9645    -1.334660 #>  ds 4 parent    0    104.7  1.011e+02   3.60049 8.0554     0.446965 #>  ds 4 parent    0     88.3  1.011e+02 -12.79951 8.0554    -1.588930 #>  ds 4 parent    1     94.2  9.755e+01  -3.35176 7.7762    -0.431030 #>  ds 4 parent    1     94.6  9.755e+01  -2.95176 7.7762    -0.379591 #>  ds 4 parent    3     78.1  9.095e+01 -12.85198 7.2570    -1.770981 #>  ds 4 parent    3     96.5  9.095e+01   5.54802 7.2570     0.764508 #>  ds 4 parent    7     76.2  7.949e+01  -3.29267 6.3569    -0.517966 #>  ds 4 parent    7     77.8  7.949e+01  -1.69267 6.3569    -0.266272 #>  ds 4 parent   14     70.8  6.384e+01   6.95621 5.1321     1.355423 #>  ds 4 parent   14     67.3  6.384e+01   3.45621 5.1321     0.673445 #>  ds 4 parent   28     43.1  4.345e+01  -0.35291 3.5515    -0.099370 #>  ds 4 parent   28     45.1  4.345e+01   1.64709 3.5515     0.463771 #>  ds 4 parent   60     21.3  2.137e+01  -0.07478 1.9063    -0.039229 #>  ds 4 parent   60     23.5  2.137e+01   2.12522 1.9063     1.114813 #>  ds 4 parent   90     11.8  1.205e+01  -0.24925 1.2957    -0.192375 #>  ds 4 parent   90     12.1  1.205e+01   0.05075 1.2957     0.039168 #>  ds 4 parent  120      7.0  6.967e+00   0.03315 1.0356     0.032013 #>  ds 4 parent  120      6.2  6.967e+00  -0.76685 1.0356    -0.740510 #>  ds 4     m1    0      1.6  1.421e-13   1.60000 0.8763     1.825856 #>  ds 4     m1    1      0.9  7.250e-01   0.17503 0.8782     0.199310 #>  ds 4     m1    3      3.7  2.038e+00   1.66201 0.8910     1.865236 #>  ds 4     m1    3      2.0  2.038e+00  -0.03799 0.8910    -0.042637 #>  ds 4     m1    7      3.6  4.186e+00  -0.58623 0.9369    -0.625692 #>  ds 4     m1    7      3.8  4.186e+00  -0.38623 0.9369    -0.412230 #>  ds 4     m1   14      7.1  6.752e+00   0.34768 1.0266     0.338666 #>  ds 4     m1   14      6.6  6.752e+00  -0.15232 1.0266    -0.148372 #>  ds 4     m1   28      9.5  9.034e+00   0.46628 1.1313     0.412159 #>  ds 4     m1   28      9.3  9.034e+00   0.26628 1.1313     0.235373 #>  ds 4     m1   60      8.3  8.634e+00  -0.33359 1.1115    -0.300112 #>  ds 4     m1   60      9.0  8.634e+00   0.36641 1.1115     0.329645 #>  ds 4     m1   90      6.6  6.671e+00  -0.07091 1.0233    -0.069295 #>  ds 4     m1   90      7.7  6.671e+00   1.02909 1.0233     1.005691 #>  ds 4     m1  120      3.7  4.823e+00  -1.12301 0.9559    -1.174763 #>  ds 4     m1  120      3.5  4.823e+00  -1.32301 0.9559    -1.383979 #>  ds 5 parent    0    110.4  1.011e+02   9.30049 8.0554     1.154563 #>  ds 5 parent    0    112.1  1.011e+02  11.00049 8.0554     1.365601 #>  ds 5 parent    1     93.5  9.440e+01  -0.90098 7.5282    -0.119681 #>  ds 5 parent    1     91.0  9.440e+01  -3.40098 7.5282    -0.451764 #>  ds 5 parent    3     71.0  8.287e+01 -11.86698 6.6217    -1.792122 #>  ds 5 parent    3     89.7  8.287e+01   6.83302 6.6217     1.031907 #>  ds 5 parent    7     60.4  6.562e+01  -5.22329 5.2711    -0.990936 #>  ds 5 parent    7     59.1  6.562e+01  -6.52329 5.2711    -1.237566 #>  ds 5 parent   14     56.5  4.739e+01   9.10588 3.8548     2.362225 #>  ds 5 parent   14     47.0  4.739e+01  -0.39412 3.8548    -0.102240 #>  ds 5 parent   28     30.2  3.118e+01  -0.98128 2.6206    -0.374451 #>  ds 5 parent   28     23.9  3.118e+01  -7.28128 2.6206    -2.778500 #>  ds 5 parent   60     17.0  1.804e+01  -1.03959 1.6761    -0.620224 #>  ds 5 parent   60     18.7  1.804e+01   0.66041 1.6761     0.394008 #>  ds 5 parent   90     11.3  1.165e+01  -0.35248 1.2727    -0.276958 #>  ds 5 parent   90     11.9  1.165e+01   0.24752 1.2727     0.194488 #>  ds 5 parent  120      9.0  7.556e+00   1.44368 1.0612     1.360449 #>  ds 5 parent  120      8.1  7.556e+00   0.54368 1.0612     0.512338 #>  ds 5     m1    0      0.7 -1.421e-14   0.70000 0.8763     0.798812 #>  ds 5     m1    1      3.0  3.160e+00  -0.15979 0.9113    -0.175340 #>  ds 5     m1    1      2.6  3.160e+00  -0.55979 0.9113    -0.614254 #>  ds 5     m1    3      5.1  8.448e+00  -3.34789 1.1026    -3.036487 #>  ds 5     m1    3      7.5  8.448e+00  -0.94789 1.1026    -0.859720 #>  ds 5     m1    7     16.5  1.581e+01   0.68760 1.5286     0.449839 #>  ds 5     m1    7     19.0  1.581e+01   3.18760 1.5286     2.085373 #>  ds 5     m1   14     22.9  2.218e+01   0.71983 1.9632     0.366658 #>  ds 5     m1   14     23.2  2.218e+01   1.01983 1.9632     0.519469 #>  ds 5     m1   28     22.2  2.425e+01  -2.05105 2.1113    -0.971479 #>  ds 5     m1   28     24.4  2.425e+01   0.14895 2.1113     0.070552 #>  ds 5     m1   60     15.5  1.876e+01  -3.25968 1.7250    -1.889646 #>  ds 5     m1   60     19.8  1.876e+01   1.04032 1.7250     0.603074 #>  ds 5     m1   90     14.9  1.365e+01   1.25477 1.3914     0.901806 #>  ds 5     m1   90     14.2  1.365e+01   0.55477 1.3914     0.398714 #>  ds 5     m1  120     10.9  9.726e+00   1.17443 1.1667     1.006587 #>  ds 5     m1  120     10.4  9.726e+00   0.67443 1.1667     0.578044 # Add a correlation between random effects of g and k2 cov_model_3 <- f_saem_dfop_sfo_2$so@model@covariance.model cov_model_3[\"log_k2\", \"g_qlogis\"] <- 1 cov_model_3[\"g_qlogis\", \"log_k2\"] <- 1 f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo,   covariance.model = cov_model_3) intervals(f_saem_dfop_sfo_3) #> Approximate 95% confidence intervals #>  #>  Fixed effects: #>                      lower         est.        upper #> parent_0       98.42519529 101.51623115 104.60726702 #> k_m1            0.01505059   0.01662123   0.01835577 #> f_parent_to_m1  0.20100222   0.27477835   0.36332008 #> k1              0.07347479   0.10139028   0.13991179 #> k2              0.01469861   0.01771120   0.02134125 #> g               0.35506898   0.46263682   0.57379888 #>  #>  Random effects: #>                             lower       est.     upper #> sd(f_parent_qlogis)    0.16472883  0.4435866 0.7224443 #> sd(log_k1)             0.05323856  0.2981783 0.5431180 #> sd(log_k2)             0.05013379  0.1912531 0.3323723 #> sd(g_qlogis)           0.04710647  0.3997298 0.7523531 #> corr(log_k2,g_qlogis) -1.31087397 -0.5845703 0.1417334 #>  #>   #>          lower       est.      upper #> a.1 0.67769608 0.87421677 1.07073746 #> b.1 0.06525119 0.07925135 0.09325151 # The correlation does not improve the fit judged by AIC and BIC, although # the likelihood is higher with the additional parameter anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3) #> Data: 171 observations of 2 variable(s) grouped in 5 datasets #>  #>                   npar    AIC    BIC     Lik #> f_saem_dfop_sfo_2   12 806.91 802.23 -391.46 #> f_saem_dfop_sfo_3   13 807.96 802.88 -390.98 #> f_saem_dfop_sfo     14 810.83 805.36 -391.41 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":null,"dir":"Reference","previous_headings":"","what":"Display the output of a summary function according to the output format — summary_listing","title":"Display the output of a summary function according to the output format — summary_listing","text":"function intended use R markdown code chunk chunk option results = \"asis\".","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display the output of a summary function according to the output format — summary_listing","text":"","code":"summary_listing(object, caption = NULL, label = NULL, clearpage = TRUE)  tex_listing(object, caption = NULL, label = NULL, clearpage = TRUE)  html_listing(object, caption = NULL)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display the output of a summary function according to the output format — summary_listing","text":"object object summary listed caption optional caption label optional label, ignored html output clearpage new page started listing? Ignored html output","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"12 datasets generated using four different models three different  variance components. four models either SFO DFOP model either  two sequential two parallel metabolites. Variance component '' based normal distribution standard deviation 3,  Variance component 'b' also based normal distribution, standard deviation 7.  Variance component 'c' based error model Rocke Lorenzato (1995),  minimum standard deviation (small y values) 0.5, proportionality constant 0.07  increase standard deviation y. Note simplified version  error model proposed Rocke Lorenzato (1995), model error  measured values approximates lognormal distribution high values, whereas using  normally distributed error components along. Initial concentrations metabolites values adding variance component resulted  value assumed limit detection 0.1 set NA. example, first dataset title SFO_lin_a based SFO model  two sequential metabolites (linear pathway), added variance component ''. Compare also code example section see degradation models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"","code":"synthetic_data_for_UBA_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"list containing twelve datasets R6 class defined mkinds,   containing, among others, following components title name dataset, e.g. SFO_lin_a data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452 Rocke, David M. und Lorenzato, Stefan (1995) two-component model   measurement error analytical chemistry. Technometrics 37(2), 176-184.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"","code":"# \\dontrun{ # The data have been generated using the following kinetic models m_synth_SFO_lin <- mkinmod(parent = list(type = \"SFO\", to = \"M1\"),                            M1 = list(type = \"SFO\", to = \"M2\"),                            M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   m_synth_SFO_par <- mkinmod(parent = list(type = \"SFO\", to = c(\"M1\", \"M2\"),                                          sink = FALSE),                            M1 = list(type = \"SFO\"),                            M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_DFOP_lin <- mkinmod(parent = list(type = \"DFOP\", to = \"M1\"),                             M1 = list(type = \"SFO\", to = \"M2\"),                             M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_DFOP_par <- mkinmod(parent = list(type = \"DFOP\", to = c(\"M1\", \"M2\"),                                           sink = FALSE),                             M1 = list(type = \"SFO\"),                             M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  # The model predictions without intentional error were generated as follows sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)  d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,                                c(k_parent = 0.7, f_parent_to_M1 = 0.8,                                  k_M1 = 0.3, f_M1_to_M2 = 0.7,                                  k_M2 = 0.02),                                c(parent = 100, M1 = 0, M2 = 0),                                sampling_times)  d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,                                 c(k1 = 0.2, k2 = 0.02, g = 0.5,                                   f_parent_to_M1 = 0.5, k_M1 = 0.3,                                   f_M1_to_M2 = 0.7, k_M2 = 0.02),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,                                c(k_parent = 0.2,                                  f_parent_to_M1 = 0.8, k_M1 = 0.01,                                  f_parent_to_M2 = 0.2, k_M2 = 0.02),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,                                c(k1 = 0.3, k2 = 0.02, g = 0.7,                                  f_parent_to_M1 = 0.6, k_M1 = 0.04,                                  f_parent_to_M2 = 0.4, k_M2 = 0.01),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  # Construct names for datasets with errors d_synth_names = paste0(\"d_synth_\", c(\"SFO_lin\", \"SFO_par\",                                      \"DFOP_lin\", \"DFOP_par\"))  # Original function used or adding errors. The add_err function now published # with this package is a slightly generalised version where the names of # secondary compartments that should have an initial value of zero (M1 and M2 # in this case) are not hardcoded any more. # add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789) # { #   set.seed(seed) #   d_long = mkin_wide_to_long(d, time = \"time\") #   d_rep = data.frame(lapply(d_long, rep, each = 2)) #   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value)) # #   d_rep[d_rep$time == 0 & d_rep$name %in% c(\"M1\", \"M2\"), \"value\"] <- 0 #   d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value)) #   d_NA$value <- round(d_NA$value, 1) #   return(d_NA) # }  # The following is the simplified version of the two-component model of Rocke # and Lorenzato (1995) sdfunc_twocomp = function(value, sd_low, rsd_high) {   sqrt(sd_low^2 + value^2 * rsd_high^2) }  # Add the errors. for (d_synth_name in d_synth_names) {   d_synth = get(d_synth_name)   assign(paste0(d_synth_name, \"_a\"), add_err(d_synth, function(value) 3))   assign(paste0(d_synth_name, \"_b\"), add_err(d_synth, function(value) 7))   assign(paste0(d_synth_name, \"_c\"), add_err(d_synth,                            function(value) sdfunc_twocomp(value, 0.5, 0.07)))  }  d_synth_err_names = c(   paste(rep(d_synth_names, each = 3), letters[1:3], sep = \"_\") )  # This is just one example of an evaluation using the kinetic model used for # the generation of the data   fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,                  quiet = TRUE)   plot_sep(fit)    summary(fit) #> mkin version used for fitting:    1.2.10  #> R version used for fitting:       4.5.0  #> Date of fit:     Mon May 12 20:27:12 2025  #> Date of summary: Mon May 12 20:27:12 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1 #> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2 #>  #> Model predictions using solution type deSolve  #>  #> Fitted using 848 model solutions performed in 0.176 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>                   value   type #> parent_0       101.3500  state #> k_parent         0.1000 deparm #> k_M1             0.1001 deparm #> k_M2             0.1002 deparm #> f_parent_to_M1   0.5000 deparm #> f_M1_to_M2       0.5000 deparm #>  #> Starting values for the transformed parameters actually optimised: #>                      value lower upper #> parent_0        101.350000  -Inf   Inf #> log_k_parent     -2.302585  -Inf   Inf #> log_k_M1         -2.301586  -Inf   Inf #> log_k_M2         -2.300587  -Inf   Inf #> f_parent_qlogis   0.000000  -Inf   Inf #> f_M1_qlogis       0.000000  -Inf   Inf #>  #> Fixed parameter values: #>      value  type #> M1_0     0 state #> M2_0     0 state #>  #> Results: #>  #>        AIC      BIC    logLik #>   188.7274 200.3723 -87.36368 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>                 Estimate Std. Error   Lower    Upper #> parent_0        102.1000    1.57000 98.8600 105.3000 #> log_k_parent     -0.3020    0.03885 -0.3812  -0.2229 #> log_k_M1         -1.2070    0.07123 -1.3520  -1.0620 #> log_k_M2         -3.9010    0.06571 -4.0350  -3.7670 #> f_parent_qlogis   1.2010    0.23530  0.7216   1.6800 #> f_M1_qlogis       0.9589    0.24890  0.4520   1.4660 #> sigma             2.2730    0.25740  1.7490   2.7970 #>  #> Parameter correlation: #>                   parent_0 log_k_parent   log_k_M1   log_k_M2 f_parent_qlogis #> parent_0         1.000e+00    3.933e-01 -1.605e-01  2.819e-02      -4.624e-01 #> log_k_parent     3.933e-01    1.000e+00 -4.082e-01  7.166e-02      -5.682e-01 #> log_k_M1        -1.605e-01   -4.082e-01  1.000e+00 -3.929e-01       7.478e-01 #> log_k_M2         2.819e-02    7.166e-02 -3.929e-01  1.000e+00      -2.658e-01 #> f_parent_qlogis -4.624e-01   -5.682e-01  7.478e-01 -2.658e-01       1.000e+00 #> f_M1_qlogis      1.614e-01    4.102e-01 -8.109e-01  5.419e-01      -8.605e-01 #> sigma           -1.377e-08    7.536e-10  1.089e-08 -4.422e-08       7.124e-09 #>                 f_M1_qlogis      sigma #> parent_0          1.614e-01 -1.377e-08 #> log_k_parent      4.102e-01  7.536e-10 #> log_k_M1         -8.109e-01  1.089e-08 #> log_k_M2          5.419e-01 -4.422e-08 #> f_parent_qlogis  -8.605e-01  7.124e-09 #> f_M1_qlogis       1.000e+00 -2.685e-08 #> sigma            -2.685e-08  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       102.10000  65.000 7.281e-36 98.86000 105.30000 #> k_parent         0.73930  25.740 2.948e-23  0.68310   0.80020 #> k_M1             0.29920  14.040 1.577e-15  0.25880   0.34590 #> k_M2             0.02023  15.220 1.653e-16  0.01769   0.02312 #> f_parent_to_M1   0.76870  18.370 7.295e-19  0.67300   0.84290 #> f_M1_to_M2       0.72290  14.500 6.418e-16  0.61110   0.81240 #> sigma            2.27300   8.832 2.161e-10  1.74900   2.79700 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   8.454       6 17 #> parent     8.660       2  6 #> M1        10.583       2  5 #> M2         3.586       2  6 #>  #> Resulting formation fractions: #>                 ff #> parent_M1   0.7687 #> parent_sink 0.2313 #> M1_M2       0.7229 #> M1_sink     0.2771 #>  #> Estimated disappearance times: #>           DT50    DT90 #> parent  0.9376   3.114 #> M1      2.3170   7.697 #> M2     34.2689 113.839 #>  #> Data: #>  time variable observed  predicted residual #>     0   parent    101.5  1.021e+02 -0.56248 #>     0   parent    101.2  1.021e+02 -0.86248 #>     1   parent     53.9  4.873e+01  5.17118 #>     1   parent     47.5  4.873e+01 -1.22882 #>     3   parent     10.4  1.111e+01 -0.70773 #>     3   parent      7.6  1.111e+01 -3.50773 #>     7   parent      1.1  5.772e-01  0.52283 #>     7   parent      0.3  5.772e-01 -0.27717 #>    14   parent      3.5  3.264e-03  3.49674 #>    28   parent      3.2  1.045e-07  3.20000 #>    90   parent      0.6  9.532e-10  0.60000 #>   120   parent      3.5 -5.940e-10  3.50000 #>     1       M1     36.4  3.479e+01  1.61088 #>     1       M1     37.4  3.479e+01  2.61088 #>     3       M1     34.3  3.937e+01 -5.07027 #>     3       M1     39.8  3.937e+01  0.42973 #>     7       M1     15.1  1.549e+01 -0.38715 #>     7       M1     17.8  1.549e+01  2.31285 #>    14       M1      5.8  1.995e+00  3.80469 #>    14       M1      1.2  1.995e+00 -0.79531 #>    60       M1      0.5  2.111e-06  0.50000 #>    90       M1      3.2 -9.672e-10  3.20000 #>   120       M1      1.5  7.670e-10  1.50000 #>   120       M1      0.6  7.670e-10  0.60000 #>     1       M2      4.8  4.455e+00  0.34517 #>     3       M2     20.9  2.153e+01 -0.62527 #>     3       M2     19.3  2.153e+01 -2.22527 #>     7       M2     42.0  4.192e+01  0.07941 #>     7       M2     43.1  4.192e+01  1.17941 #>    14       M2     49.4  4.557e+01  3.83353 #>    14       M2     44.3  4.557e+01 -1.26647 #>    28       M2     34.6  3.547e+01 -0.87275 #>    28       M2     33.0  3.547e+01 -2.47275 #>    60       M2     18.8  1.858e+01  0.21837 #>    60       M2     17.6  1.858e+01 -0.98163 #>    90       M2     10.6  1.013e+01  0.47130 #>    90       M2     10.8  1.013e+01  0.67130 #>   120       M2      9.8  5.521e+00  4.27893 #>   120       M2      3.3  5.521e+00 -2.22107 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"datasets used comparative validation several kinetic evaluation  software packages (Ranke, 2014).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"","code":"test_data_from_UBA_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"list containing three datasets R6 class defined mkinds.   dataset , among others, following components title name dataset, e.g. UBA_2014_WS_river data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"","code":"# \\dontrun{   # This is a level P-II evaluation of the dataset according to the FOCUS kinetics   # guidance. Due to the strong correlation of the parameter estimates, the   # covariance matrix is not returned. Note that level P-II evaluations are   # generally considered deprecated due to the frequent occurrence of such   # large parameter correlations, among other reasons (e.g. the adequacy of the   # model).   m_ws <- mkinmod(parent_w = mkinsub(\"SFO\", \"parent_s\"),                   parent_s = mkinsub(\"SFO\", \"parent_w\")) #> Temporary DLL for differentials generated and loaded   f_river <- mkinfit(m_ws, test_data_from_UBA_2014[[1]]$data, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data   plot_sep(f_river)     summary(f_river)$bpar #>                           Estimate se_notrans    t value       Pr(>t) #> parent_w_0             95.98567441 2.16285684 44.3791159 1.245593e-17 #> k_parent_w              0.42068803 0.05573864  7.5475120 8.752928e-07 #> k_parent_s              0.07419672 0.10108562  0.7339987 2.371337e-01 #> f_parent_w_to_parent_s  0.14336920 0.05809346  2.4679062 1.305295e-02 #> f_parent_s_to_parent_w  1.00000000 3.13868615  0.3186046 3.772097e-01 #> sigma                   2.98287858 0.68923267  4.3278253 2.987160e-04 #>                              Lower       Upper #> parent_w_0             91.48420501 100.4871438 #> k_parent_w              0.36593946   0.4836276 #> k_parent_s              0.02289956   0.2404043 #> f_parent_w_to_parent_s  0.08180934   0.2391848 #> f_parent_s_to_parent_w  0.00000000   1.0000000 #> sigma                   2.00184022   3.9639169   mkinerrmin(f_river) #>             err.min n.optim df #> All data 0.09246946       5  6 #> parent_w 0.06377096       3  3 #> parent_s 0.20882325       2  3    # This is the evaluation used for the validation of software packages   # in the expertise from 2014   m_soil <- mkinmod(parent = mkinsub(\"SFO\", c(\"M1\", \"M2\")),                     M1 = mkinsub(\"SFO\", \"M3\"),                     M2 = mkinsub(\"SFO\", \"M3\"),                     M3 = mkinsub(\"SFO\"),                     use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded    f_soil <- mkinfit(m_soil, test_data_from_UBA_2014[[3]]$data, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data   plot_sep(f_soil, lpos = c(\"topright\", \"topright\", \"topright\", \"bottomright\"))    summary(f_soil)$bpar #>                   Estimate  se_notrans    t value       Pr(>t)        Lower #> parent_0       76.55425650 0.859186398 89.1008711 1.113861e-26 74.755959420 #> k_parent        0.12081956 0.004601918 26.2541722 1.077359e-16  0.111561575 #> k_M1            0.84258615 0.806159719  1.0451851 1.545266e-01  0.113779564 #> k_M2            0.04210880 0.017083034  2.4649483 1.170188e-02  0.018013857 #> k_M3            0.01122918 0.007245855  1.5497385 6.885051e-02  0.002909431 #> f_parent_to_M1  0.32240200 0.240783878  1.3389684 9.819070e-02           NA #> f_parent_to_M2  0.16099855 0.033691952  4.7785463 6.531136e-05           NA #> f_M1_to_M3      0.27921507 0.269423709  1.0363419 1.565266e-01  0.022978202 #> f_M2_to_M3      0.55641252 0.595119937  0.9349586 1.807707e-01  0.008002509 #> sigma           1.14005399 0.149696423  7.6157731 1.727024e-07  0.826735778 #>                      Upper #> parent_0       78.35255358 #> k_parent        0.13084582 #> k_M1            6.23970946 #> k_M2            0.09843260 #> k_M3            0.04333992 #> f_parent_to_M1          NA #> f_parent_to_M2          NA #> f_M1_to_M3      0.86450778 #> f_M2_to_M3      0.99489895 #> sigma           1.45337221   mkinerrmin(f_soil) #>             err.min n.optim df #> All data 0.09649963       9 20 #> parent   0.04721283       2  6 #> M1       0.26551208       2  5 #> M2       0.20327575       2  5 #> M3       0.05196550       3  4   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"transformations intended map parameters take restricted values full scale real numbers. kinetic rate constants parameters can take positive values, simple log transformation used. compositional parameters, formations fractions always sum 1 can negative, ilr transformation used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"","code":"transform_odeparms(   parms,   mkinmod,   transform_rates = TRUE,   transform_fractions = TRUE )  backtransform_odeparms(   transparms,   mkinmod,   transform_rates = TRUE,   transform_fractions = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"parms Parameters kinetic models used differential equations. mkinmod kinetic model class mkinmod, containing names model variables needed grouping formation fractions ilr transformation, parameter names information pathway sink included model. transform_rates Boolean specifying kinetic rate constants transformed model specification used fitting better compliance assumption normal distribution estimator. TRUE, also alpha beta parameters FOMC model log-transformed, well k1 k2 rate constants DFOP HS models break point tb HS model. transform_fractions Boolean specifying formation fractions constants transformed model specification used fitting better compliance assumption normal distribution estimator. default (TRUE) transformations. g parameter DFOP model also seen fraction. single fraction transformed (g parameter DFOP single target variable e.g. single metabolite plus pathway sink), logistic transformation used stats::qlogis(). cases, .e. two formation fractions need transformed whose sum exceed one, ilr transformation used. transparms Transformed parameters kinetic models used fitting procedure.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"vector transformed backtransformed parameters","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"transformation sets formation fractions fragile, supposes ordering components forward backward transformation. problem internal use mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"","code":"SFO_SFO <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = TRUE),   m1 = list(type = \"SFO\"), use_of_ff = \"min\") #> Temporary DLL for differentials generated and loaded  # Fit the model to the FOCUS example dataset D using defaults FOCUS_D <- subset(FOCUS_2006_D, value != 0) # remove zero values to avoid warning fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) fit.s <- summary(fit) # Transformed and backtransformed parameters print(fit.s$par, 3) #>                   Estimate Std. Error Lower  Upper #> parent_0             99.60     1.5702 96.40 102.79 #> log_k_parent_sink    -3.04     0.0763 -3.19  -2.88 #> log_k_parent_m1      -2.98     0.0403 -3.06  -2.90 #> log_k_m1_sink        -5.25     0.1332 -5.52  -4.98 #> sigma                 3.13     0.3585  2.40   3.85 print(fit.s$bpar, 3) #>               Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 102.7931 #> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04103   0.0560 #> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04678   0.0551 #> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069 #> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549  # \\dontrun{ # Compare to the version without transforming rate parameters (does not work # with analytical solution, we get NA values for m1 in predictions) fit.2 <- mkinfit(SFO_SFO, FOCUS_D, transform_rates = FALSE,   solution_type = \"deSolve\", quiet = TRUE) fit.2.s <- summary(fit.2) print(fit.2.s$par, 3) #>               Estimate Std. Error    Lower    Upper #> parent_0      99.59848    1.57022 96.40384 1.03e+02 #> k_parent_sink  0.04792    0.00365  0.04049 5.54e-02 #> k_parent_m1    0.05078    0.00205  0.04661 5.49e-02 #> k_m1_sink      0.00526    0.00070  0.00384 6.69e-03 #> sigma          3.12550    0.35852  2.39609 3.85e+00 print(fit.2.s$bpar, 3) #>               Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 1.03e+02 #> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04049 5.54e-02 #> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04661 5.49e-02 #> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00384 6.69e-03 #> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609 3.85e+00 # }  initials <- fit$start$value names(initials) <- rownames(fit$start) transformed <- fit$start_transformed$value names(transformed) <- rownames(fit$start_transformed) transform_odeparms(initials, SFO_SFO) #>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink  #>        100.750000         -2.302585         -2.301586         -2.300587  backtransform_odeparms(transformed, SFO_SFO) #>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink  #>      100.7500        0.1000        0.1001        0.1002   # \\dontrun{ # The case of formation fractions (this is now the default) SFO_SFO.ff <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = TRUE),   m1 = list(type = \"SFO\"),   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_D, quiet = TRUE) fit.ff.s <- summary(fit.ff) print(fit.ff.s$par, 3) #>                 Estimate Std. Error  Lower  Upper #> parent_0         99.5985     1.5702 96.404 102.79 #> log_k_parent     -2.3157     0.0409 -2.399  -2.23 #> log_k_m1         -5.2475     0.1332 -5.518  -4.98 #> f_parent_qlogis   0.0579     0.0893 -0.124   0.24 #> sigma             3.1255     0.3585  2.396   3.85 print(fit.ff.s$bpar, 3) #>                Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0       99.59848    1.57022   63.43 2.30e-36 96.40383 102.7931 #> k_parent        0.09870    0.00403   24.47 4.96e-23  0.09082   0.1073 #> k_m1            0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069 #> f_parent_to_m1  0.51448    0.02230   23.07 3.10e-22  0.46912   0.5596 #> sigma           3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549 initials <- c(\"f_parent_to_m1\" = 0.5) transformed <- transform_odeparms(initials, SFO_SFO.ff) backtransform_odeparms(transformed, SFO_SFO.ff) #> f_parent_to_m1  #>            0.5   # And without sink SFO_SFO.ff.2 <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = FALSE),   m1 = list(type = \"SFO\"),   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_D, quiet = TRUE) fit.ff.2.s <- summary(fit.ff.2) print(fit.ff.2.s$par, 3) #>              Estimate Std. Error Lower Upper #> parent_0        84.79      3.012 78.67 90.91 #> log_k_parent    -2.76      0.082 -2.92 -2.59 #> log_k_m1        -4.21      0.123 -4.46 -3.96 #> sigma            8.22      0.943  6.31 10.14 print(fit.ff.2.s$bpar, 3) #>          Estimate se_notrans t value   Pr(>t)   Lower  Upper #> parent_0  84.7916    3.01203   28.15 1.92e-25 78.6704 90.913 #> k_parent   0.0635    0.00521   12.19 2.91e-14  0.0538  0.075 #> k_m1       0.0148    0.00182    8.13 8.81e-10  0.0115  0.019 #> sigma      8.2229    0.94323    8.72 1.73e-10  6.3060 10.140 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an mkinfit model with different arguments — update.mkinfit","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"function return updated mkinfit object. fitted degradation model parameters old fit used starting values updated fit. Values specified 'parms.ini' /'state.ini' override starting values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"","code":"# S3 method for class 'mkinfit' update(object, ..., evaluate = TRUE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"object mkinfit object updated ... Arguments mkinfit replace arguments original call. Arguments set NULL remove arguments given original call evaluate call evaluated returned call","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"","code":"# \\dontrun{ fit <- mkinfit(\"SFO\", subset(FOCUS_2006_D, value != 0), quiet = TRUE) parms(fit) #>    parent_0    k_parent       sigma  #> 99.44423885  0.09793574  3.39632469  plot_err(fit)  fit_2 <- update(fit, error_model = \"tc\") parms(fit_2) #>     parent_0     k_parent    sigma_low     rsd_high  #> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02  plot_err(fit_2)  # }"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-1-2-10","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 1.2.10 (unreleased)","text":"‘R/{saem,endpoints,plot.mixed.mmkin}.R’: function ‘saem()’ gains argument ‘center_covariates’. functions ‘parms’, ‘endpoints’ ‘plot.mixed.mmkin’ adapted, user can still specify covariate values original, uncentered scale even covariate centered.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-1-2-10","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 1.2.10 (unreleased)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Make parent model selection robust ‘R/mkinpredict.R’: Predictions fits using solution type “deSolve” previously incorrect case observed data time zero (GitHub issue #16).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-1-2-9","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 1.2.9 (2024-02-13)","text":"‘R/plot.mixed.R’: Support 25 datasets ‘R/mkinfit.R’: Support passing observed data ‘tibble’ ‘R/parplot.R’: Support multistart objects covariate models filter negative values scaled parameters (warning) plotting. ’R/create_deg_func.R: Make sure reversible reactions specified case two observed variables, supported","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-128-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.8 (unreleased)","title":"mkin 1.2.8 (unreleased)","text":"‘R/{mhmkin,status}.R’: Deal ‘saem’ fits fail updating ‘mhmkin’ object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-127-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.7 (unreleased)","title":"mkin 1.2.7 (unreleased)","text":"‘R/illparms.R’: Fix bug prevented ill-defined random effect found one random effect model. Also add test .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-126-2023-10-14","dir":"Changelog","previous_headings":"","what":"mkin 1.2.6 (2023-10-14)","title":"mkin 1.2.6 (2023-10-14)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Fix erroneous call ‘endpoints()’ function","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-125-2023-08-09","dir":"Changelog","previous_headings":"","what":"mkin 1.2.5 (2023-08-09)","title":"mkin 1.2.5 (2023-08-09)","text":"‘vignettes/mesotrione_parent_2023.rnw’: Prebuilt vignette showing covariate modelling can done relevant parent degradation models. ‘inst/testdata/mesotrione_soil_efsa_2016}.xlsx’: Another example spreadsheets use ‘read_spreadsheet()’, featuring pH dependent degradation R/illparms.R: Fix detection ill-defined slope error model parameters case estimate negative","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-124-2023-05-19","dir":"Changelog","previous_headings":"","what":"mkin 1.2.4 (2023-05-19)","title":"mkin 1.2.4 (2023-05-19)","text":"R/endpoints.R: Fix calculation endpoints user specified covariate values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-1231-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.3.1 (unreleased)","title":"mkin 1.2.3.1 (unreleased)","text":"Small fixes get online docs right (example code R/hierarchical_kinetics, cluster setup cyantraniliprole dmta pathway vignettes, graphics model comparison multistart vignette), rebuild online docs","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-123-2023-04-17","dir":"Changelog","previous_headings":"","what":"mkin 1.2.3 (2023-04-17)","title":"mkin 1.2.3 (2023-04-17)","text":"‘R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R’: Calculate parameters endpoints plot population curves specific covariate values, specific percentiles covariate values used saem fits. Depend current deSolve version possibility avoid resolving symbols shared library (compiled models) , thanks Thomas Petzoldt. ‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Start new cluster creating model stored user specified location, otherwise symbols found worker processes. ‘tests/testthat/test_compiled_symbols.R’: new tests control problems may introduced possibility use pre-resolved symbols. ‘R/mkinerrmin.R’: Fix typo subset (use = instead ==), thanks Sebastian Meyer spotting work R 4.3.0.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-122-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.2 (unreleased)","title":"mkin 1.2.2 (unreleased)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics’: R markdown template facilitate application hierarchical kinetic models. ‘inst/testdata/{cyantraniliprole_soil_efsa_2014,lambda-cyhalothrin_soil_efsa_2014}.xlsx’: Example spreadsheets use ‘read_spreadsheet()’. ‘R/mhmkin.R’: Allow ‘illparms.mhmkin’ object list suitable dimensions value argument ‘no_random_effects’, making possible exclude random effects ill-defined simpler variants set degradation models. Remove possibility exclude random effects based separate fits, work well. ‘R/summary.saem.mmkin.R’: List initial parameter values summary, including random effects error model parameters. Avoid redundant warnings occurred calculation correlations fixed effects case Fisher information matrix inverted. List correlations random effects specified user covariance model. ‘R/parplot.R’: Possibility select top ‘llquant’ fraction fits parameter plots, improved legend text. ‘R/illparms.R’: Also check confidence intervals slope parameters covariate models include zero. implemented fits obtained saemix backend. ‘R/parplot.R’: Make function work also case multistart runs failed. ‘R/intervals.R’: Include correlations random effects model case .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-121-2022-11-19","dir":"Changelog","previous_headings":"","what":"mkin 1.2.1 (2022-11-19)","title":"mkin 1.2.1 (2022-11-19)","text":"‘{data,R}/ds_mixed.rda’: Include test data package instead generating ‘tests/testthat/setup_script.R’. Refactor generating code make consistent update tests. ‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect DFOP fit ‘saem’, attempt avoid platform dependence surfaced Fedora systems CRAN check farm ‘tests/testthat/test_mixed.R’: Round parameters found saemix two significant digits printing, also help avoid platform dependence tests ‘R/saem.R’: Fix bug prevented ‘error.ini’ passed ‘saemix_model’, set default c(1, 1) avoid changing test results ‘R/parplot.R’: Show initial values error model parameters ‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute resulting ‘logLik’ object, order make test_AIC.R succeed current R-devel","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-120-2022-11-17","dir":"Changelog","previous_headings":"","what":"mkin 1.2.0 (2022-11-17)","title":"mkin 1.2.0 (2022-11-17)","text":"‘R/saem.R’: ‘logLik’, ‘update’ ‘anova’ methods ‘saem.mmkin’ objects. ‘R/saem.R’: Automatic estimation start parameters random effects case mkin transformations, nicely improving convergence reducing problems iterative ODE solutions. ‘R/status.R’: New generic show status information fit array objects methods ‘mmkin’, ‘mhmkin’ ‘multistart’ objects. ‘R/mhmkin.R’: New method performing multiple hierarchical mkin fits one function call, optionally parallel. ‘R/mhmkin.R’: ‘anova.mhmkin’ conveniently comparing resulting fits. ‘R/illparms.R’: New generic show ill-defined parameters methods ‘mkinfit’, ‘mmkin’, ‘saem.mmkin’ ‘mhmkin’ objects. ‘R/multistart.R’: New method testing multiple start parameters hierarchical model fits, function ‘llhist’ new generic ‘parplot’ diagnostics, new generics ‘.best’ ‘best’ extracting fit highest likelihood ‘R/summary.mmkin.R’: Summary method mmkin objects. ‘R/saem.R’: Implement test saemix transformations FOMC HS. Also, error saemix transformations requested supported. ‘R/read_spreadsheet.R’: Conveniently read data spreadsheet file. ‘R/tex_listings.R’: Conveniently include summaries fit objects R markdown documents compiled LaTeX.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-111-2022-07-12","dir":"Changelog","previous_headings":"","what":"mkin 1.1.1 (2022-07-12)","title":"mkin 1.1.1 (2022-07-12)","text":"’R/{mkinmod,mkinpredict}.R: Store DLL information mkinmod objects use information mkinpredict avoid performance regression brought bugfix R 4.2.x. Thanks Tomas Kalibera analysis problem r-package-devel list suggestion fix . ‘vignettes/FOCUS_L.rmd’: Remove outdated note referring failure calculate covariance matrix DFOP L2 dataset. Since 0.9.45.5 covariance matrix available ‘vignettes/web_only/benchmarks.rmd’: Add first benchmark data using laptop system, therefore add CPU showing benchmark results. ‘dimethenamid_2018’: Update example code use saemix ‘CAKE_export’: Check validity map argument, updates ‘saem()’: Slightly improve speed case analytical solution saemix implemented, activate test respective code ‘mean_degparms’: New argument ‘default_log_parms’ makes possible supply surrogate value (default) log parameters fail t-test ‘plot.mixed.mmkin’: Pass frame argument also residual plots, take ‘default_log_parms’ argument ‘mean_degparms’ used constructing approximate population curves, plot population curve last avoid covered data ‘plot.mkinfit’: Respect argument ‘maxabs’ residual plots, make possible give ylim list, row layouts","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mixed-effects-models-1-1-0","dir":"Changelog","previous_headings":"","what":"Mixed-effects models","title":"mkin 1.1.0 (2022-03-14)","text":"Reintroduce interface saemix version 3.0 (now CRAN), particular generic function ‘saem’ generator ‘saem.mmkin’, currently using ‘saemix_model’ ‘saemix_data’, summary plot methods ‘mean_degparms’: New argument ‘test_log_parms’ makes function consider log-transformed parameters untransformed parameters pass t-test certain confidence level. can used obtain plausible starting parameters different mixed-effects model backends ‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ ‘conf.level’ ‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations dimethenamid data. ‘intervals’: Provide method nlme function ‘saem.mmkin’ objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-105-2021-09-15","dir":"Changelog","previous_headings":"","what":"mkin 1.0.5 (2021-09-15)","title":"mkin 1.0.5 (2021-09-15)","text":"‘dimethenamid_2018’: Correct data Borstel soil. five observations Staudenmaier (2013) previously stored “Borstel 2” actually just subset 16 observations “Borstel 1” now simply “Borstel”","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-104-2021-04-20","dir":"Changelog","previous_headings":"","what":"mkin 1.0.4 (2021-04-20)","title":"mkin 1.0.4 (2021-04-20)","text":"plotting functions setting graphical parameters: Use .exit() resetting graphical parameters ‘plot.mkinfit’: Use xlab xlim residual plot show_residuals TRUE ‘mmkin’: Use cores = 1 per default Windows make easier first time users","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-103-2021-02-15","dir":"Changelog","previous_headings":"","what":"mkin 1.0.3 (2021-02-15)","title":"mkin 1.0.3 (2021-02-15)","text":"Review update README, ‘Introduction mkin’ vignette help pages","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-102-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.0.2 (Unreleased)","title":"mkin 1.0.2 (Unreleased)","text":"‘mkinfit’: Keep model names stored ‘mkinmod’ objects, avoiding loss ‘gmkin’","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-101-2021-02-10","dir":"Changelog","previous_headings":"","what":"mkin 1.0.1 (2021-02-10)","title":"mkin 1.0.1 (2021-02-10)","text":"‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code dontrun sections failed current defaults ‘logLik.mkinfit’: Improve example code avoid warnings show convenient syntax ‘mkinresplot’: Re-add Katrin Lindenberger coauthor accidentally removed long ago Remove tests relying non-convergence FOMC fit FOCUS dataset platform dependent (revealed new additional tests CRAN, thanks!) Increase test tolerance parameter comparisons also proved platform dependent","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"general-1-0-0","dir":"Changelog","previous_headings":"","what":"General","title":"mkin 1.0.0 (2021-02-03)","text":"‘mkinmod’ models gain arguments ‘name’ ‘dll_dir’ , conjunction current version ‘inline’ package, make possible still use DLL used fast ODE solutions ‘deSolve’ saving restoring ‘mkinmod’ object. ‘mkindsg’ R6 class groups ‘mkinds’ datasets metadata ‘f_norm_temp_focus’ generic function normalise time intervals using FOCUS method, methods numeric vectors ‘mkindsg’ objects ‘D24_2014’ ‘dimethenamid_2018’ datasets ‘focus_soil_moisture’ FOCUS default soil moisture data ‘update’ method ‘mmkin’ objects ‘transform_odeparms’, ‘backtransform_odeparms’: Use logit transformation solitary fractions like g parameter DFOP model, formation fractions pathway one target variable ‘plot.mmkin’: Add ylab argument, making possible customize y axis label panels left without affecting residual plots. Reduce legend size vertical distance panels ‘plot.mkinfit’: Change default ylab “Observed” “Residue”. Pass xlab residual plot show_residuals TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mixed-effects-models-1-0-0","dir":"Changelog","previous_headings":"","what":"Mixed-effects models","title":"mkin 1.0.0 (2021-02-03)","text":"‘mixed.mmkin’ New container mmkin objects plotting ‘plot.mixed.mmkin’ method ‘plot.mixed.mmkin’ method used ‘nlme.mmkin’ inheriting ‘mixed.mmkin’ (currently virtual) ‘plot’, ‘summary’ ‘print’ methods ‘nlme.mmkin’ objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09503-2020-10-08","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.3 (2020-10-08)","title":"mkin 0.9.50.3 (2020-10-08)","text":"‘parms’: Add method mmkin objects ‘mmkin’ ‘confint(method = ’profile’): Use cores detected parallel::detectCores() per default ‘confint(method = ’profile’): Choose accuracy based ‘rel_tol’ argument, relative bounds obtained quadratic approximation ‘mkinfit’: Make ‘use_of_ff’ = “max” also default models specified using short names like “SFO” “FOMC” ‘mkinfit’: Run ‘stats::shapiro.test()’ standardized residuals warn p < 0.05 ‘mkinfit’: ‘error_model_algorithm’ = ‘d_3’ fail direct fitting fails, reports results threestep algorithm returned ‘mmkin’: fail one fits fails, assign try-error respective position mmkin object ‘mkinfit’: Ignore components state.ini correspond state variables model ‘endpoints’: Back-calculate DT50 value DT90 also biphasic models DFOP, HS SFORB","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09502-2020-05-12","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.2 (2020-05-12)","title":"mkin 0.9.50.2 (2020-05-12)","text":"Increase tolerance platform specific test results Solaris test machine CRAN Updates corrections (using spelling package) documentation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09501-2020-05-11","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.1 (2020-05-11)","title":"mkin 0.9.50.1 (2020-05-11)","text":"Support SFORB formation fractions ‘mkinmod’: Make ‘use_of_ff’ = “max” default Improve performance ) avoiding expensive calls cost function like merge() data.frame(), b) implementing analytical solutions SFO-SFO DFOP-SFO","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-094911-2020-04-20","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.11 (2020-04-20)","title":"mkin 0.9.49.11 (2020-04-20)","text":"Increase test tolerance make pass CRAN check machines","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-094910-2020-04-18","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.10 (2020-04-18)","title":"mkin 0.9.49.10 (2020-04-18)","text":"‘nlme.mmkin’: nlme method mmkin row objects associated S3 class print, plot, anova endpoint methods ‘mean_degparms, nlme_data, nlme_function’: Three new functions facilitate building nlme models mmkin row objects ‘endpoints’: Don’t return SFORB list component ’s empty. reduces distraction complies documentation Article compiled models: Add platform specific code suppress warnings zero values removed FOCUS D dataset ‘plot.mmkin’: Add argument ‘standardized’ avoid warnings occurred passed part additional arguments captured dots (…) ‘summary.mkinfit’: Add AIC, BIC log likelihood summary","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09499-2020-03-31","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.9 (2020-03-31)","title":"mkin 0.9.49.9 (2020-03-31)","text":"‘mkinmod’: Use pkgbuild::has_compiler instead Sys.(‘gcc’), latter often fail even Rtools installed ‘mkinds’: Use roxygen documenting fields methods R6 class","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09498-2020-01-09","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.8 (2020-01-09)","title":"mkin 0.9.49.8 (2020-01-09)","text":"‘aw’: Generic function calculating Akaike weights, methods mkinfit objects mmkin columns ‘loftest’: Add lack--fit test ‘plot_res’, ‘plot_sep’ ‘mkinerrplot’: Add possibility show standardized residuals make default fits error models ‘const’ ‘lrtest.mkinfit’: Improve naming compared fits case fixed parameters ‘confint.mkinfit’: Make quadratic approximation default, likelihood profiling takes lot time, especially fit three parameters","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09497-2019-11-01","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.7 (2019-11-01)","title":"mkin 0.9.49.7 (2019-11-01)","text":"Fix bug introduced 0.9.49.6 occurred direct optimisation yielded higher likelihood three-step optimisation d_3 algorithm, caused fitted parameters three-step optimisation returned instead parameters direct optimisation Add ‘nobs’ method mkinfit objects, enabling default ‘BIC’ method stats package. Also, add ‘BIC’ method mmkin column objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09496-2019-10-31","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.6 (2019-10-31)","title":"mkin 0.9.49.6 (2019-10-31)","text":"Implement likelihood ratio test method ‘lrtest’ lmtest package Add ‘update’ method mkinfit objects remembers fitted parameters appropriate Add ‘residuals’ method mkinfit objects supports scaling based error model Fix bug ‘mkinfit’ prevented summaries objects fitted fixed parameters generated Add ‘parms’ ‘confint’ methods mkinfit objects. Confidence intervals based quadratic approximation summary, based profile likelihood Move long-running tests tests/testthat/slow separate test log. currently take around 7 minutes system ‘mkinfit’: Clean code return functions calculate log-likelihood sum squared residuals Vignette ‘twa.html’: Add maximum time weighted average formulas hockey stick model Support frameless plots (‘frame = FALSE’) Support suppress chi2 error level (‘show_errmin = FALSE’) ‘plot.mmkin’ Update README introductory vignette Report ‘OLS’ error_model_algorithm summary case default error_model (‘const’) used Support summarizing ‘mkinfit’ objects generated versions < 0.9.49.5","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09495-2019-07-04","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.5 (2019-07-04)","title":"mkin 0.9.49.5 (2019-07-04)","text":"Several algorithms minimization negative log-likelihood non-constant error models (two-component variance variable). case error model constant variance, least squares used stable. default algorithm ‘d_3’ tries direct minimization three-step procedure, returns model highest likelihood. argument ‘reweight.method’ mkinfit mmkin now obsolete, use ‘error_model’ ‘error_model_algorithm’ instead Add test checks get best known AIC parent fits 12 test datasets. Add test datasets purpose. New function ‘mkinerrplot’. function also used residual plots ‘plot.mmkin’ argument ‘resplot = “errmod”’ given, ‘plot.mkinfit’ ‘show_errplot’ set TRUE. Remove dependency FME, use nlminb optimisation (‘Port’ algorithm). remember cases one optimisation algorithms preferable, except sometime used Levenberg-Marquardt speed cases expect get trapped local minimum. Use numDeriv package calculate hessians. results slightly different confidence intervals, takes bit longer, apparently robust Add simple benchmark vignette document impact performance. code manual weighting removed. functionality might get added later time. time , please use earlier version, e.g. 0.9.48.1 want manual weighting. fitting time reported summary now includes time used calculation hessians Adapt tests Fix error FOCUS chi2 error level calculations occurred parameters specified parms.ini model. warning already issued, fitting parallel via mmkin go unnoticed. Add example datasets obtained risk assessment reports published European Food Safety Agency.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09481-2019-03-04","dir":"Changelog","previous_headings":"","what":"mkin 0.9.48.1 (2019-03-04)","title":"mkin 0.9.48.1 (2019-03-04)","text":"Add function ‘logLik.mkinfit’ makes possible calculate AIC mkinfit objects Add function ‘AIC.mmkin’ make easy compare columns mmkin objects ‘add_err’: Respect argument giving number replicates synthetic dataset ‘max_twa_parent’: Support maximum time weighted average concentration calculations hockey stick (HS) model ‘mkinpredict’: Make function generic create method mkinfit objects ‘mkinfit’: Improve correctness fitted two component error model fitting mean absolute deviance observation observed values, weighting current two-component error model ‘tests/testthat/test_irls.R’: Test components error model used generate data can reproduced moderate accuracy Add function ‘CAKE_export’ facilitate cross-checking results Implement logistic model (tested parent fits) ‘nafta’: Add evaluations according NAFTA guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09475-2018-09-14","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.5 (2018-09-14)","title":"mkin 0.9.47.5 (2018-09-14)","text":"Make two-component error model stop cases inadequate avoid nls crashes windows Move two vignettes location built CRAN (avoid NOTES long execution times) Exclude example code testing CRAN avoid NOTES long execution times","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09473","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.3","title":"mkin 0.9.47.3","text":"‘mkinfit’: Improve fitting error model reweight.method = ‘tc’. Add ‘manual’ possible arguments ‘weight’ Test FOCUS_2006_C can evaluated DFOP reweight.method = ‘tc’","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09472-2018-07-19","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.2 (2018-07-19)","title":"mkin 0.9.47.2 (2018-07-19)","text":"‘sigma_twocomp’: Rename ‘sigma_rl’ ‘sigma_twocomp’ Rocke Lorenzato model assumes lognormal distribution large y. Correct references Rocke Lorenzato model accordingly. ‘mkinfit’: Use 1.1 starting value N parameter IORE models obtain convergence difficult cases. Show parameter names ‘trace_parms’ ‘TRUE’.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09471-2018-02-06","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.1 (2018-02-06)","title":"mkin 0.9.47.1 (2018-02-06)","text":"Skip tests CRAN winbuilder avoid timeouts ‘test_data_from_UBA_2014’: Added list datasets containing experimental data used expertise 2014 ‘mkinfit’: Added iterative reweighting method ‘tc’ using two-component error model Rocke Lorenzato. NA values data returned . ‘mkinfit’: Work around bug current FME version prevented convergence message returned case non-convergence. ‘summary.mkinfit’: Improved output regarding weighting method. predictions returned NA values model (see ). ‘summary.mkinfit’: Show versions mkin R used fitting (ones used summary) fit generated mkin >= 0.9.47.1","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09463-2017-11-16","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.3 (2017-11-16)","title":"mkin 0.9.46.3 (2017-11-16)","text":"README.md, vignettes/mkin.Rmd: URLs updated synthetic_data_for_UBA: Add code used generate data interest reproducibility","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09462-2017-10-10","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.2 (2017-10-10)","title":"mkin 0.9.46.2 (2017-10-10)","text":"Converted vignette FOCUS_Z tex/pdf markdown/html DESCRIPTION: Add ORCID","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09461-2017-09-14","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.1 (2017-09-14)","title":"mkin 0.9.46.1 (2017-09-14)","text":"plot.mkinfit: Fix scaling residual plots case separate plots observed variable plot.mkinfit: Use data points fitted curve y axis scaling case separate plots observed variable Documentation updates","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-0946-2017-07-24","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46 (2017-07-24)","title":"mkin 0.9.46 (2017-07-24)","text":"Remove test_FOMC_ill-defined.R platform dependent","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09452-2017-07-24","dir":"Changelog","previous_headings":"","what":"mkin 0.9.45.2 (2017-07-24)","title":"mkin 0.9.45.2 (2017-07-24)","text":"Rename twa max_twa_parent avoid conflict twa pfm package Update URLs documentation Limit test code one core pass windows Switch microbenchmark rbenchmark former supported platforms","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-45-1","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9.45.1 (2016-12-20)","text":"twa function, calculating maximum time weighted average concentrations parent (SFO, FOMC DFOP).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-45","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.45 (2016-12-08)","text":"plot.mkinfit plot.mmkin: plotting device tikz, LaTeX markup used chi2 error graphs. Use pkgdown, successor staticdocs generating static HTML documentation. Include example output graphs also dontrun sections. plot.mkinfit: Plotting fail compiled model available, e.g. removed temporary directory. case, uncompiled model now used plotting","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-44","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9.44 (2016-06-29)","text":"test test_FOMC_ill-defined failed several architectures, test now skipped","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-43","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9.43 (2016-06-28)","text":"title changed Kinetic evaluations chemical degradation data plot.mkinfit: Add possibility show fits (residual plots requested) separately observed variables plot.mkinfit: Add possibility show chi2 error levels plot, similar way shown plot.mmkin plot_sep: Add function convenience wrapper plotting observed variables mkinfit objects separately, chi2 error values residual plots. Vignettes: main vignette mkin converted R markdown updated. vignettes also updated show current improved functionality. function add_err added package, making easy generate simulated data using error model based normal distribution","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-43","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.43 (2016-06-28)","text":"Remove outdated reference inline package compiled_models vignette mkinfit: error cases fit converges, Jacobian untransformed model cost can estimated. Give warning instead return NA t-test results. summary.mkinfit: Give warning message covariance matrix can obtained. test added containing corresponding edge case check warnings correctly issued fit terminate. plot.mmkin: Round chi2 error value three significant digits, instead two decimal digits. mkinfit: Return err values used weighted fits column named err. Also include inverse weights column value observed data used, returned observed data component mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-43","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9.43 (2016-06-28)","text":"endpoints: name substance degrading metabolite (e.g. parent compound) used model formulation ended letter f, rate parameters listed formation fractions mixed names. also appear summary. mkinfit: Check observed variables checking user tried fix formation fractions fitting using ilr transformation. plot.mmkin: Set plot margins correctly, also case single fit plotted, main title placed reasonable way. plot.mkinfit: Correct default values col_obs, pch_obs lty_obs case obs_vars specified.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-42","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9.42 (2016-03-25)","text":"Add argument from_max_mean mkinfit, fitting decline maximum observed value models single observed variable","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-42","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.42 (2016-03-25)","text":"Add plots compiled_models vignette Give explanatory error message mkinmod fails due missing definition target variable print.mkinmod(): Improve formatting printing mkinmod model definitions","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-41","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-41 (2015-11-09)","text":"Add R6 class mkinds representing datasets printing method Add printing method mkinmod objects Make possible specify arbitrary strings names compounds mkinmod, show plot Use index.r file group help topics static documentation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-41","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-41 (2015-11-09)","text":"print.summary.mkinfit(): Avoid error occurred printing summaries generated mkin versions 0.9-36","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-40","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-40 (2015-07-21)","text":"endpoints(): DFOP SFORB models, optimize() used, make use fact DT50 must DT50_k1 DT50_k2 (DFOP) DT50_b1 DT50_b2 (SFORB), optimize() sometimes find minimum. Likewise finding DT90 values. Also fit log scale make function efficient.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"internal-changes-0-9-40","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"mkin 0.9-40 (2015-07-21)","text":"DESCRIPTION, NAMESPACE, R/*.R: Import () stats, graphics methods packages, qualify function calls non-base packages installed R avoid NOTES made R CMD check –-cran upcoming R versions.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-39","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-39 (2015-06-26)","text":"New function mmkin(): function takes character vector model shorthand names, alternatively list mkinmod models, well list dataset main arguments. returns matrix mkinfit objects, row model column dataset. subsetting method single brackets available. Fitting models parallel using parallel package supported. New function plot.mmkin(): Plots single-row single-column mmkin objects including residual plots.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-39","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-39 (2015-06-26)","text":"mkinparplot(): Fix x axis scaling rate constants formation fractions got confused introduction t-values transformed parameters.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-38","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-38 (2015-06-24)","text":"vignettes/compiled_models.html: Show performance improvement factor actually obtained building vignette, well mkin version, system info CPU model used building vignette. GNUMakefile,vignettes/*: Clean vignette generation include table contents HTML vignettes.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-38","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-38 (2015-06-24)","text":"mkinmod(): generating C code derivatives, declare time variable needed remove ‘-W--unused-variable’ compiler flag C compiler used CRAN checks Solaris know .","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-36","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-36 (2015-06-21)","text":"summary.mkinfit(): one-sided t-test significant difference untransformed parameters zero now always shown, based assumption normal distribution estimators untransformed parameters. Use caution, assumption unrealistic e.g. rate constants nonlinear kinetic models. compiler (gcc) installed, use version differential equation model compiled C code, huge performance boost models deSolve method works. mkinmod(): Create list component $cf (class CFuncList) list returned mkinmod, version can compiled autogenerated C code (see ). mkinfit(): Set default solution_type deSolve compiled version model present, except analytical solution possible.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-36","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-36 (2015-06-21)","text":"Added simple showcase vignette evaluation FOCUS example dataset D","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-35","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-35 (2015-05-15)","text":"Switch RUnit testthat testing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-35","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-35 (2015-05-15)","text":"mkinparplot(): Avoid warnings occurred confidence intervals available summary fit print.summary.mkinfit(): Fix printing summary case number iterations available NAMESPACE: export S3 methods plot.mkinfit, summary.mkinfit print.summary.mkinfit satisfy R CMD check R-devel mkinparplot(): Avoid warning R CMD check undeclared global variable Lower","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-35","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-35 (2015-05-15)","text":"mkinfit(): Report successful termination quiet = FALSE. helpful difficult problems fitted reweight.method = obs, progress often indicated reweighting. first test using results established expertise written German Federal Environmental Agency (UBA) added. Add synthetic datasets generated expertise written German Federal Environmental Agency UBA Add tests based datasets","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-34","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-34 (2014-11-22)","text":"Add convenience function mkinsub() creating lists used mkinmod() Add possibility fit indeterminate order rate equation (IORE) models using analytical solution (parent ) numeric solution. Paths IORE compounds metabolites supported using formation fractions (use_of_ff = ‘max’). Note numerical solution (method.ode = ‘deSolve’) IORE differential equations sometimes fails due numerical problems. Switch using Port algorithm (using model/trust region approach) per default. needing iterations Levenberg-Marquardt algorithm previously used per default, less sensitive starting parameters.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-34","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-34 (2014-11-22)","text":"formatting differential equations summary improved Always include 0 y axis plotting fit","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-33","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-33 (2014-10-22)","text":"initial value (state.ini) observed variable highest observed residue set 100 case time zero observation state.ini = \"auto\" basic unit test mkinerrmin() written","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-33","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-33 (2014-10-22)","text":"mkinfit(): internally fitted parameter g named g_ilr even transform_fractions=FALSE mkinfit(): initial value (state.ini) parent compound set parent () variable highest value observed data. mkinerrmin(): checking degrees freedom metabolites, check time zero value fixed instead checking observed value zero. ensures correct calculation degrees freedom also cases metabolite residue time zero greater zero. plot.mkinfit(): Avoid warning message using first component ylim occurred ylim specified explicitly","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-33","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-33 (2014-10-22)","text":"formatting differential equations summary improved wrapping overly long lines FOCUS_Z vignette rebuilt improvement using width 70 avoid output outside grey area print.summary.mkinfit(): Avoid warning occurred gmkin showed summaries initial fits without iterations mkinfit(): Avoid warning occurred summarising fit performed maxitmodFit = 0 done gmkin configuring new fits.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-32","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-32 (2014-07-24)","text":"number degrees freedom difficult define case ilr transformation formation fractions. Now source compartment number ilr parameters (=number optimised parameters) divided number pathways metabolites (=number affected data series) leads fractional degrees freedom cases. default initial value first state value now taken mean observations time zero, available. kinetic model can alternatively specified shorthand name parent degradation models, e.g. SFO, DFOP. Optimisation method, number model evaluations time elapsed optimisation given summary mkinfit objects. maximum number iterations optimisation algorithm can specified using argument maxit.modFit mkinfit function. mkinfit gives warning fit converge (apply SANN method). warning included summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-32","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-32 (2014-07-24)","text":"Avoid plotting artificial 0 residual time zero mkinresplot determination degrees freedom mkinerrmin, formation fractions accounted multiple times case parallel formation metabolites. See new feature described solution. transform_rates=FALSE mkinfit now also works FOMC HS models. Initial values formation fractions set cases. warning given fit converge method default Levenberg-Marquardt method Marq used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-32","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-32 (2014-07-24)","text":"Vignettes rebuilt reflect changes summary method. Algorithm Pseudo excluded needs user-defined parameter limits supported. Algorithm Newton excluded different way specify maximum number iterations appear provide additional benefits.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-31","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-31 (2014-07-14)","text":"internal renaming optimised parameters Version 0.9-30 led errors determination degrees freedom chi2 error level calulations mkinerrmin() used summary function.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-30","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-30 (2014-07-11)","text":"now possible use formation fractions combination turning sink mkinmod().","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-30","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-30 (2014-07-11)","text":"original transformed parameters now different names (e.g. k_parent log_k_parent. also differ many formation fractions pathway sink. order information blocks print.summary.mkinfit.R() ordered logical way.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-30","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-30 (2014-07-11)","text":"vignette FOCUS_Z simplified use formation fractions turning sink, slightly amended use new versions DT50 values calculated since mkin 0.9-29. vignettes rebuilt reflect changes. ChangeLog renamed NEWS.md entries converted markdown syntax compatible tools::news() function built R. test suite overhauled. Tests DFOP SFORB models dataset FOCUS_2006_A removed, much dependent optimisation algorithm /starting parameters, dataset SFO (compare kinfit vignette). Also, Schaefer complex case can now fitted using formation fractions, ‘Port’ optimisation method also fit A2 way published Piacenza paper. checks introduced mkinfit(), leading warnings stopping execution unsupported combinations methods parameters requested.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-29-2014-06-27","dir":"Changelog","previous_headings":"","what":"mkin 0.9-29 (2014-06-27)","title":"mkin 0.9-29 (2014-06-27)","text":"R/mkinresplot.R: Make possible specify xlim R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values FOMC, DFOP, HS SFORB. Avoid calculation formation fractions rate constants directly fitted","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-28-2014-05-20","dir":"Changelog","previous_headings":"","what":"mkin 0.9-28 (2014-05-20)","title":"mkin 0.9-28 (2014-05-20)","text":"backtransform confidence intervals formation fractions one compound formed, parameters define pathways set Add historical remarks background main package vignette Correct ‘isotropic’ ‘isometric’ ilr transformation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-27-2014-05-10","dir":"Changelog","previous_headings":"","what":"mkin 0.9-27 (2014-05-10)","title":"mkin 0.9-27 (2014-05-10)","text":"Fork GUI separate package gmkin DESCRIPTION, NAMESPACE, TODO: Adapt add copyright information Remove files belonging GUI Possibility fit without parameter transformations, using bounds implemented FME Add McCall 2,4,5-T dataset Enable selection observed variables plotting Add possibility show residual plot plot.mkinfit R/mkinparplot.R, man/mkinparplot.Rd: plot parameters confidence intervals Change vignette format Sweave knitr Split examples vignette FOCUS_L FOCUS_Z Remove warning constant formation fractions mkinmod based misconception Restrict unit test Schaefer data parent primary metabolites formation fraction DT50 A2 highly correlated passing test platform dependent. example, test fails 1 14 platforms CRAN today. Add Eurofins Regulatory AG copyright notices Import FME deSolve instead depending clean startup Add starter function GUI: gmkin() Change format workspace files gmkin can distributed documented package Add gmkin workspace datasets FOCUS_2006_gmkin FOCUS_2006_Z_gmkin","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-24-2013-11-06","dir":"Changelog","previous_headings":"","what":"mkin 0.9-24 (2013-11-06)","title":"mkin 0.9-24 (2013-11-06)","text":"Bugfix re-enabling fixing combination initial values state variables Default values kinetic rate constants 0.1 “salted” small increment avoid numeric artefacts eigenvalue based solutions Backtransform fixed ODE parameters summary","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-22-2013-10-26","dir":"Changelog","previous_headings":"","what":"mkin 0.9-22 (2013-10-26)","title":"mkin 0.9-22 (2013-10-26)","text":"Get rid optimisation step mkinerrmin - unnecessary. Thanks KinGUII inspiration - actually equation 6-2 FOCUS kinetics p. 91 overlooked originally Fix plot.mkinfit passed graphical arguments like main solver use plot=TRUE mkinfit() example first successful fits simple GUI Fix iteratively reweighted least squares case many metabolites Unify naming initial values state variables Unify naming dataframes optimised fixed parameters summary Show weighting method residuals summary Correct output data case manual weighting Implement IRLS assuming different variances observed variables use 0 values time zero chi2 error level calculations. way done KinGUII makes sense. impact chi2 error levels output. Generally seem lower metabolites now, presumably mean observed values higher detailed list changes mkin source please consult commit history http://github.com/jranke/mkin","code":""}]
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