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-rw-r--r--GNUmakefile2
-rw-r--r--docs/articles/FOCUS_D.html8
-rw-r--r--docs/articles/FOCUS_L.html42
-rw-r--r--docs/articles/mkin.html2
-rw-r--r--docs/articles/twa.html2
-rw-r--r--docs/articles/web_only/FOCUS_Z.html2
-rw-r--r--docs/articles/web_only/NAFTA_examples.html2
-rw-r--r--docs/articles/web_only/benchmarks.html24
-rw-r--r--docs/articles/web_only/compiled_models.html12
-rw-r--r--docs/reference/index.html2
-rw-r--r--docs/reference/mkinfit.html28
-rw-r--r--docs/reference/mkinmod.html2
-rw-r--r--docs/reference/mkinpredict.html6
-rw-r--r--docs/reference/mmkin.html4
-rw-r--r--docs/reference/summary.mkinfit.html6
-rw-r--r--docs/reference/synthetic_data_for_UBA_2014-1.pngbin0 -> 62975 bytes
-rw-r--r--docs/reference/synthetic_data_for_UBA_2014.html462
-rw-r--r--docs/sitemap.xml2
-rw-r--r--man/synthetic_data_for_UBA_2014.Rd292
-rw-r--r--vignettes/mkin_benchmarks.rdabin880 -> 875 bytes
20 files changed, 681 insertions, 219 deletions
diff --git a/GNUmakefile b/GNUmakefile
index 816cf1fb..0084267a 100644
--- a/GNUmakefile
+++ b/GNUmakefile
@@ -87,7 +87,7 @@ vignettes/web_only/%.html: vignettes/references.bib vignettes/web_only/%.Rmd
articles: vignettes/web_only/FOCUS_Z.html vignettes/web_only/compiled_models.html
pd:
- "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE)"
+ "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE, lazy = TRUE)"
git add -A
git commit -m 'Static documentation rebuilt by pkgdown' -e
diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html
index 53164ffd..f8c51e2d 100644
--- a/docs/articles/FOCUS_D.html
+++ b/docs/articles/FOCUS_D.html
@@ -90,7 +90,7 @@
<h1>Example evaluation of FOCUS Example Dataset D</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>FOCUS_D.Rmd</code></div>
@@ -170,8 +170,8 @@
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(fit)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:07 2019
-## Date of summary: Wed Sep 18 18:24:08 2019
+## Date of fit: Thu Sep 19 09:52:55 2019
+## Date of summary: Thu Sep 19 09:52:56 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -179,7 +179,7 @@
##
## Model predictions using solution type deSolve
##
-## Fitted using 389 model solutions performed in 1.014 s
+## Fitted using 389 model solutions performed in 1.001 s
##
## Error model: Constant variance
##
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 50c83db6..7b35beeb 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -90,7 +90,7 @@
<h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>FOCUS_L.Rmd</code></div>
@@ -116,8 +116,8 @@
<a class="sourceLine" id="cb2-2" title="2"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(m.L1.SFO)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:09 2019
-## Date of summary: Wed Sep 18 18:24:09 2019
+## Date of fit: Thu Sep 19 09:52:57 2019
+## Date of summary: Thu Sep 19 09:52:57 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent
@@ -218,8 +218,8 @@
## finite result is doubtful</code></pre>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:12 2019
-## Date of summary: Wed Sep 18 18:24:12 2019
+## Date of fit: Thu Sep 19 09:52:59 2019
+## Date of summary: Thu Sep 19 09:52:59 2019
##
##
## Warning: Optimisation did not converge:
@@ -231,7 +231,7 @@
##
## Model predictions using solution type analytical
##
-## Fitted using 899 model solutions performed in 1.954 s
+## Fitted using 899 model solutions performed in 1.908 s
##
## Error model: Constant variance
##
@@ -323,15 +323,15 @@
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(m.L2.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:13 2019
-## Date of summary: Wed Sep 18 18:24:13 2019
+## Date of fit: Thu Sep 19 09:53:00 2019
+## Date of summary: Thu Sep 19 09:53:00 2019
##
## Equations:
## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
##
## Model predictions using solution type analytical
##
-## Fitted using 239 model solutions performed in 0.497 s
+## Fitted using 239 model solutions performed in 0.49 s
##
## Error model: Constant variance
##
@@ -399,8 +399,8 @@
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(m.L2.DFOP, <span class="dt">data =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:14 2019
-## Date of summary: Wed Sep 18 18:24:14 2019
+## Date of fit: Thu Sep 19 09:53:02 2019
+## Date of summary: Thu Sep 19 09:53:02 2019
##
## Equations:
## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -409,7 +409,7 @@
##
## Model predictions using solution type analytical
##
-## Fitted using 572 model solutions performed in 1.215 s
+## Fitted using 572 model solutions performed in 1.204 s
##
## Error model: Constant variance
##
@@ -499,8 +499,8 @@
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:16 2019
-## Date of summary: Wed Sep 18 18:24:16 2019
+## Date of fit: Thu Sep 19 09:53:04 2019
+## Date of summary: Thu Sep 19 09:53:04 2019
##
## Equations:
## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -509,7 +509,7 @@
##
## Model predictions using solution type analytical
##
-## Fitted using 373 model solutions performed in 0.778 s
+## Fitted using 373 model solutions performed in 0.799 s
##
## Error model: Constant variance
##
@@ -605,15 +605,15 @@
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:17 2019
-## Date of summary: Wed Sep 18 18:24:17 2019
+## Date of fit: Thu Sep 19 09:53:04 2019
+## Date of summary: Thu Sep 19 09:53:05 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent
##
## Model predictions using solution type analytical
##
-## Fitted using 142 model solutions performed in 0.293 s
+## Fitted using 142 model solutions performed in 0.287 s
##
## Error model: Constant variance
##
@@ -670,15 +670,15 @@
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.49.6
## R version used for fitting: 3.6.1
-## Date of fit: Wed Sep 18 18:24:17 2019
-## Date of summary: Wed Sep 18 18:24:17 2019
+## Date of fit: Thu Sep 19 09:53:05 2019
+## Date of summary: Thu Sep 19 09:53:05 2019
##
## Equations:
## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
##
## Model predictions using solution type analytical
##
-## Fitted using 224 model solutions performed in 0.46 s
+## Fitted using 224 model solutions performed in 0.48 s
##
## Error model: Constant variance
##
diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 90549b0f..ec138595 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -90,7 +90,7 @@
<h1>Introduction to mkin</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>mkin.Rmd</code></div>
diff --git a/docs/articles/twa.html b/docs/articles/twa.html
index 2d44a89d..9cb5873a 100644
--- a/docs/articles/twa.html
+++ b/docs/articles/twa.html
@@ -90,7 +90,7 @@
<h1>Calculation of time weighted average concentrations with mkin</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>twa.Rmd</code></div>
diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html
index aed1debc..5ea0b256 100644
--- a/docs/articles/web_only/FOCUS_Z.html
+++ b/docs/articles/web_only/FOCUS_Z.html
@@ -90,7 +90,7 @@
<h1>Example evaluation of FOCUS dataset Z</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>FOCUS_Z.Rmd</code></div>
diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html
index 3ca93667..0476acf8 100644
--- a/docs/articles/web_only/NAFTA_examples.html
+++ b/docs/articles/web_only/NAFTA_examples.html
@@ -90,7 +90,7 @@
<h1>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>NAFTA_examples.Rmd</code></div>
diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index d46e738a..c0682cb0 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -90,7 +90,7 @@
<h1>Benchmark timings for mkin on various systems</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@@ -204,77 +204,77 @@
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 5.970
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 6.005
## t2
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 36.524
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 37.266
## t3
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.607
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.559
## t4
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 8.461
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 31.617
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 31.574
## t5
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 9.495
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 6.395
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 5.675
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.386
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.002
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.543
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.659
## t6
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 2.623
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 2.542
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 2.723
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 2.643
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.635
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 2.579
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 2.634
## t7
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.341
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.324
## t8
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 7.525
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.632
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.862
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.02
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.737
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 8.101
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 8.233
## t9
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 8.171
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.618
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 7.763
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 16.211
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 16.376
## t10
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 8.041
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 8.058
## t11
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 5.636
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 5.574
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.365
## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.626
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.804</code></pre>
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 11.133</code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/save.html">save</a></span>(mkin_benchmarks, <span class="dt">file =</span> <span class="st">"~/git/mkin/vignettes/mkin_benchmarks.rda"</span>)</a></code></pre></div>
</div>
</div>
diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html
index 1876e2b3..6df504ed 100644
--- a/docs/articles/web_only/compiled_models.html
+++ b/docs/articles/web_only/compiled_models.html
@@ -90,7 +90,7 @@
<h1>Performance benefit by using compiled model definitions in mkin</h1>
<h4 class="author">Johannes Ranke</h4>
- <h4 class="date">2019-09-18</h4>
+ <h4 class="date">2019-09-19</h4>
<div class="hidden name"><code>compiled_models.Rmd</code></div>
@@ -165,9 +165,9 @@
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
## = TRUE): Observations with value of zero were removed from the data</code></pre>
<pre><code>## test replications elapsed relative user.self sys.self
-## 3 deSolve, compiled 3 3.129 1.000 3.127 0
-## 1 deSolve, not compiled 3 28.841 9.217 28.827 0
-## 2 Eigenvalue based 3 4.433 1.417 4.430 0
+## 3 deSolve, compiled 3 3.171 1.000 3.170 0
+## 1 deSolve, not compiled 3 29.012 9.149 28.996 0
+## 2 Eigenvalue based 3 4.473 1.411 4.470 0
## user.child sys.child
## 3 0 0
## 1 0 0
@@ -216,8 +216,8 @@
## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
## value of zero were removed from the data</code></pre>
<pre><code>## test replications elapsed relative user.self sys.self
-## 2 deSolve, compiled 3 4.860 1.000 4.857 0
-## 1 deSolve, not compiled 3 53.877 11.086 53.850 0
+## 2 deSolve, compiled 3 4.913 1.000 4.909 0
+## 1 deSolve, not compiled 3 53.418 10.873 53.393 0
## user.child sys.child
## 2 0 0
## 1 0 0</code></pre>
diff --git a/docs/reference/index.html b/docs/reference/index.html
index b86df636..a8b326d7 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -311,7 +311,7 @@
</tr><tr>
<td>
- <p><code><a href="synthetic_data_for_UBA.html">synthetic_data_for_UBA_2014</a></code> </p>
+ <p><code><a href="synthetic_data_for_UBA_2014.html">synthetic_data_for_UBA_2014</a></code> </p>
</td>
<td><p>Synthetic datasets for one parent compound with two metabolites</p></td>
</tr><tr>
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index dc052f2a..0767d2f8 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -408,15 +408,15 @@ Per default, parameters in the kinetic models are internally transformed in
<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.49.6
#&gt; R version used for fitting: 3.6.1
-#&gt; Date of fit: Wed Sep 18 18:22:06 2019
-#&gt; Date of summary: Wed Sep 18 18:22:06 2019
+#&gt; Date of fit: Thu Sep 19 09:50:54 2019
+#&gt; Date of summary: Thu Sep 19 09:50:54 2019
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted using 222 model solutions performed in 0.459 s
+#&gt; Fitted using 222 model solutions performed in 0.458 s
#&gt;
#&gt; Error model: Constant variance
#&gt;
@@ -490,7 +490,7 @@ Per default, parameters in the kinetic models are internally transformed in
<span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>,
<span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)))</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; User System verstrichen
-#&gt; 1.494 0.000 1.496 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; NULL</div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
+#&gt; 1.479 0.002 1.482 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; NULL</div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
#&gt; parent_sink parent_m1 m1_sink
#&gt; 0.485524 0.514476 1.000000
#&gt;
@@ -563,7 +563,7 @@ Per default, parameters in the kinetic models are internally transformed in
#&gt; Sum of squared residuals at call 126: 371.2134
#&gt; Sum of squared residuals at call 135: 371.2134
#&gt; Negative log-likelihood at call 145: 97.22429</div><div class='output co'>#&gt; <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#&gt; User System verstrichen
-#&gt; 1.056 0.000 1.057 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; NULL</div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; $ff
+#&gt; 1.053 0.000 1.054 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; NULL</div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; $ff
#&gt; parent_sink parent_m1 m1_sink
#&gt; 0.485524 0.514476 1.000000
#&gt;
@@ -599,8 +599,8 @@ Per default, parameters in the kinetic models are internally transformed in
<span class='no'>SFO_SFO.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
<span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.49.6
#&gt; R version used for fitting: 3.6.1
-#&gt; Date of fit: Wed Sep 18 18:22:22 2019
-#&gt; Date of summary: Wed Sep 18 18:22:22 2019
+#&gt; Date of fit: Thu Sep 19 09:51:10 2019
+#&gt; Date of summary: Thu Sep 19 09:51:10 2019
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
@@ -608,7 +608,7 @@ Per default, parameters in the kinetic models are internally transformed in
#&gt;
#&gt; Model predictions using solution type deSolve
#&gt;
-#&gt; Fitted using 421 model solutions performed in 1.15 s
+#&gt; Fitted using 421 model solutions performed in 1.138 s
#&gt;
#&gt; Error model: Constant variance
#&gt;
@@ -718,8 +718,8 @@ Per default, parameters in the kinetic models are internally transformed in
#&gt; 120 m1 25.15 28.78984 -3.640e+00
#&gt; 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.obs</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.49.6
#&gt; R version used for fitting: 3.6.1
-#&gt; Date of fit: Wed Sep 18 18:22:24 2019
-#&gt; Date of summary: Wed Sep 18 18:22:24 2019
+#&gt; Date of fit: Thu Sep 19 09:51:12 2019
+#&gt; Date of summary: Thu Sep 19 09:51:12 2019
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
@@ -727,7 +727,7 @@ Per default, parameters in the kinetic models are internally transformed in
#&gt;
#&gt; Model predictions using solution type deSolve
#&gt;
-#&gt; Fitted using 979 model solutions performed in 2.553 s
+#&gt; Fitted using 979 model solutions performed in 2.565 s
#&gt;
#&gt; Error model: Variance unique to each observed variable
#&gt;
@@ -850,8 +850,8 @@ Per default, parameters in the kinetic models are internally transformed in
#&gt; 120 m1 25.15 28.80429 -3.654e+00
#&gt; 120 m1 33.31 28.80429 4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.tc</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.49.6
#&gt; R version used for fitting: 3.6.1
-#&gt; Date of fit: Wed Sep 18 18:22:34 2019
-#&gt; Date of summary: Wed Sep 18 18:22:34 2019
+#&gt; Date of fit: Thu Sep 19 09:51:22 2019
+#&gt; Date of summary: Thu Sep 19 09:51:22 2019
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
@@ -859,7 +859,7 @@ Per default, parameters in the kinetic models are internally transformed in
#&gt;
#&gt; Model predictions using solution type deSolve
#&gt;
-#&gt; Fitted using 2289 model solutions performed in 9.369 s
+#&gt; Fitted using 2289 model solutions performed in 9.24 s
#&gt;
#&gt; Error model: Two-component variance function
#&gt;
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index a5cdd04d..f592af1c 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -239,7 +239,7 @@ For the definition of model types and their parameters, the equations given
<span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(
<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
<span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; Compilation argument:
-#&gt; /usr/lib/R/bin/R CMD SHLIB file310b6c1fede0.c 2&gt; file310b6c1fede0.c.err.txt
+#&gt; /usr/lib/R/bin/R CMD SHLIB file663b71dc323f.c 2&gt; file663b71dc323f.c.err.txt
#&gt; Program source:
#&gt; 1: #include &lt;R.h&gt;
#&gt; 2:
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html
index 97d3ca3a..5faff3c7 100644
--- a/docs/reference/mkinpredict.html
+++ b/docs/reference/mkinpredict.html
@@ -332,17 +332,17 @@
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
<span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#&gt; time parent m1
#&gt; 201 20 4.978707 27.46227</div><div class='output co'>#&gt; User System verstrichen
-#&gt; 0.003 0.000 0.004 </div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(
+#&gt; 0.004 0.000 0.004 </div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
<span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#&gt; time parent m1
#&gt; 201 20 4.978707 27.46227</div><div class='output co'>#&gt; User System verstrichen
-#&gt; 0.001 0.000 0.002 </div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(
+#&gt; 0.002 0.000 0.002 </div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
<span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#&gt; time parent m1
#&gt; 201 20 4.978707 27.46227</div><div class='output co'>#&gt; User System verstrichen
-#&gt; 0.021 0.000 0.022 </div><div class='input'>
+#&gt; 0.022 0.000 0.021 </div><div class='input'>
<span class='co'># \dontrun{</span>
<span class='co'># Predict from a fitted model</span>
<span class='no'>f</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_C</span>)</div><div class='output co'>#&gt; <span class='message'>Ordinary least squares optimisation</span></div><div class='output co'>#&gt; Sum of squared residuals at call 1: 552.5739
diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html
index 81ebde7c..0f384062 100644
--- a/docs/reference/mmkin.html
+++ b/docs/reference/mmkin.html
@@ -199,8 +199,8 @@
<span class='no'>time_1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fits.4</span> <span class='kw'>&lt;-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
<span class='no'>time_default</span></div><div class='output co'>#&gt; User System verstrichen
-#&gt; 0.013 0.039 5.128 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#&gt; User System verstrichen
-#&gt; 18.942 0.000 18.951 </div><div class='input'>
+#&gt; 0.016 0.029 4.960 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#&gt; User System verstrichen
+#&gt; 19.084 0.004 19.099 </div><div class='input'>
<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]])</div><div class='output co'>#&gt; $ff
#&gt; parent_M1 parent_sink M1_M2 M1_sink
#&gt; 0.7340481 0.2659519 0.7505684 0.2494316
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index c5d58911..73410181 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -215,15 +215,15 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)), <span class='no'>FOCUS_2006_A</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.49.6
#&gt; R version used for fitting: 3.6.1
-#&gt; Date of fit: Wed Sep 18 18:23:52 2019
-#&gt; Date of summary: Wed Sep 18 18:23:52 2019
+#&gt; Date of fit: Thu Sep 19 09:52:40 2019
+#&gt; Date of summary: Thu Sep 19 09:52:40 2019
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent_sink * parent
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted using 131 model solutions performed in 0.268 s
+#&gt; Fitted using 131 model solutions performed in 0.27 s
#&gt;
#&gt; Error model: Constant variance
#&gt;
diff --git a/docs/reference/synthetic_data_for_UBA_2014-1.png b/docs/reference/synthetic_data_for_UBA_2014-1.png
new file mode 100644
index 00000000..9d8c0931
--- /dev/null
+++ b/docs/reference/synthetic_data_for_UBA_2014-1.png
Binary files differ
diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html
new file mode 100644
index 00000000..2c4480d9
--- /dev/null
+++ b/docs/reference/synthetic_data_for_UBA_2014.html
@@ -0,0 +1,462 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+<title>Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin</title>
+
+<!-- jquery -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
+<!-- Bootstrap -->
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous" />
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
+
+<!-- Font Awesome icons -->
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/all.min.css" integrity="sha256-nAmazAk6vS34Xqo0BSrTb+abbtFlgsFK7NKSi6o7Y78=" crossorigin="anonymous" />
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/v4-shims.min.css" integrity="sha256-6qHlizsOWFskGlwVOKuns+D1nB6ssZrHQrNj1wGplHc=" crossorigin="anonymous" />
+
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
+
+<!-- headroom.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/headroom.min.js" integrity="sha256-DJFC1kqIhelURkuza0AvYal5RxMtpzLjFhsnVIeuk+U=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
+
+<!-- pkgdown -->
+<link href="../pkgdown.css" rel="stylesheet">
+<script src="../pkgdown.js"></script>
+
+
+
+<meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014" />
+
+<meta property="og:description" content="The 12 datasets were generated using four different models and three different
+ variance components. The four models are either the SFO or the DFOP model with either
+ two sequential or two parallel metabolites.
+Variance component 'a' is based on a normal distribution with standard deviation of 3,
+ Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
+ Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
+ minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
+ for the increase of the standard deviation with y. Note that this is a simplified version
+ of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
+ measured values approximates lognormal distribution for high values, whereas we are using
+ normally distributed error components all along.
+Initial concentrations for metabolites and all values where adding the variance component resulted
+ in a value below the assumed limit of detection of 0.1 were set to NA.
+As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
+ with two sequential metabolites (linear pathway), with added variance component 'a'.
+Compare also the code in the example section to see the degradation models." />
+<meta name="twitter:card" content="summary" />
+
+
+
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+ <h1>Synthetic datasets for one parent compound with two metabolites</h1>
+
+ <div class="hidden name"><code>synthetic_data_for_UBA_2014.Rd</code></div>
+ </div>
+
+ <div class="ref-description">
+
+ <p>The 12 datasets were generated using four different models and three different
+ variance components. The four models are either the SFO or the DFOP model with either
+ two sequential or two parallel metabolites.</p>
+<p>Variance component 'a' is based on a normal distribution with standard deviation of 3,
+ Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
+ Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
+ minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
+ for the increase of the standard deviation with y. Note that this is a simplified version
+ of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
+ measured values approximates lognormal distribution for high values, whereas we are using
+ normally distributed error components all along.</p>
+<p>Initial concentrations for metabolites and all values where adding the variance component resulted
+ in a value below the assumed limit of detection of 0.1 were set to <code>NA</code>.</p>
+<p>As an example, the first dataset has the title <code>SFO_lin_a</code> and is based on the SFO model
+ with two sequential metabolites (linear pathway), with added variance component 'a'.</p>
+<p>Compare also the code in the example section to see the degradation models.</p>
+
+ </div>
+
+ <pre class="usage"><span class='no'>synthetic_data_for_UBA_2014</span></pre>
+
+ <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
+
+ <p>A list containing twelve datasets as an R6 class defined by <code><a href='mkinds.html'>mkinds</a></code>,
+ each containing, among others, the following components</p><dl class='dl-horizontal'>
+ <dt><code>title</code></dt><dd><p>The name of the dataset, e.g. <code>SFO_lin_a</code></p></dd>
+ <dt><code>data</code></dt><dd><p>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></p></dd>
+
+</dl>
+
+
+ <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
+
+ <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
+ zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
+<p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
+ measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p>
+
+
+ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+ <pre class="examples"><div class='input'><span class='co'># The data have been generated using the following kinetic models</span>
+<span class='no'>m_synth_SFO_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M1"</span>),
+ <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M2"</span>),
+ <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+
+<span class='no'>m_synth_SFO_par</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>),
+ <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
+ <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
+ <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+<span class='no'>m_synth_DFOP_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"DFOP"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M1"</span>),
+ <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M2"</span>),
+ <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+<span class='no'>m_synth_DFOP_par</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"DFOP"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>),
+ <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
+ <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
+ <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+<span class='co'># The model predictions without intentional error were generated as follows</span>
+<span class='no'>sampling_times</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span>)
+
+<span class='no'>d_synth_SFO_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_SFO_lin</span>,
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.7</span>, <span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.8</span>,
+ <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.3</span>, <span class='kw'>f_M1_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.7</span>,
+ <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
+ <span class='no'>sampling_times</span>)
+
+<span class='no'>d_synth_DFOP_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_DFOP_lin</span>,
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k1</span> <span class='kw'>=</span> <span class='fl'>0.2</span>, <span class='kw'>k2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>, <span class='kw'>g</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
+ <span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.3</span>,
+ <span class='kw'>f_M1_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.7</span>, <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
+ <span class='no'>sampling_times</span>)
+
+<span class='no'>d_synth_SFO_par</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_SFO_par</span>,
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.2</span>,
+ <span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.8</span>, <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.01</span>,
+ <span class='kw'>f_parent_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.2</span>, <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
+ <span class='no'>sampling_times</span>)
+
+<span class='no'>d_synth_DFOP_par</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_DFOP_par</span>,
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k1</span> <span class='kw'>=</span> <span class='fl'>0.3</span>, <span class='kw'>k2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>, <span class='kw'>g</span> <span class='kw'>=</span> <span class='fl'>0.7</span>,
+ <span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.6</span>, <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.04</span>,
+ <span class='kw'>f_parent_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.4</span>, <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
+ <span class='no'>sampling_times</span>)
+
+<span class='co'># Construct names for datasets with errors</span>
+<span class='no'>d_synth_names</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='st'>"d_synth_"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO_lin"</span>, <span class='st'>"SFO_par"</span>,
+ <span class='st'>"DFOP_lin"</span>, <span class='st'>"DFOP_par"</span>))
+
+<span class='co'># Original function used or adding errors. The add_err function now published</span>
+<span class='co'># with this package is a slightly generalised version where the names of</span>
+<span class='co'># secondary compartments that should have an initial value of zero (M1 and M2</span>
+<span class='co'># in this case) are not hardcoded any more.</span>
+<span class='co'># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span>
+<span class='co'># {</span>
+<span class='co'># set.seed(seed)</span>
+<span class='co'># d_long = mkin_wide_to_long(d, time = "time")</span>
+<span class='co'># d_rep = data.frame(lapply(d_long, rep, each = 2))</span>
+<span class='co'># d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span>
+<span class='co'>#</span>
+<span class='co'># d_rep[d_rep$time == 0 &amp; match(d_rep$name, c("M1", "M2"), "value"] &lt;- 0</span>
+<span class='co'># d_NA &lt;- transform(d_rep, value = ifelse(value &lt; LOD, NA, value))</span>
+<span class='co'># d_NA$value &lt;- round(d_NA$value, 1)</span>
+<span class='co'># return(d_NA)</span>
+<span class='co'># }</span>
+
+<span class='co'># The following is the simplified version of the two-component model of Rocke</span>
+<span class='co'># and Lorenzato (1995)</span>
+<span class='no'>sdfunc_twocomp</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>value</span>, <span class='no'>sd_low</span>, <span class='no'>rsd_high</span>) {
+ <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>sqrt</a></span>(<span class='no'>sd_low</span>^<span class='fl'>2</span> + <span class='no'>value</span>^<span class='fl'>2</span> * <span class='no'>rsd_high</span>^<span class='fl'>2</span>)
+}
+
+<span class='co'># Add the errors.</span>
+<span class='kw'>for</span> (<span class='no'>d_synth_name</span> <span class='kw'>in</span> <span class='no'>d_synth_names</span>)
+{
+ <span class='no'>d_synth</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/get.html'>get</a></span>(<span class='no'>d_synth_name</span>)
+ <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='no'>d_synth_name</span>, <span class='st'>"_a"</span>), <span class='fu'><a href='add_err.html'>add_err</a></span>(<span class='no'>d_synth</span>, <span class='kw'>function</span>(<span class='no'>value</span>) <span class='fl'>3</span>))
+ <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='no'>d_synth_name</span>, <span class='st'>"_b"</span>), <span class='fu'><a href='add_err.html'>add_err</a></span>(<span class='no'>d_synth</span>, <span class='kw'>function</span>(<span class='no'>value</span>) <span class='fl'>7</span>))
+ <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='no'>d_synth_name</span>, <span class='st'>"_c"</span>), <span class='fu'><a href='add_err.html'>add_err</a></span>(<span class='no'>d_synth</span>,
+ <span class='kw'>function</span>(<span class='no'>value</span>) <span class='fu'>sdfunc_twocomp</span>(<span class='no'>value</span>, <span class='fl'>0.5</span>, <span class='fl'>0.07</span>)))
+
+}
+
+<span class='no'>d_synth_err_names</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(
+ <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='no'>d_synth_names</span>, <span class='kw'>each</span> <span class='kw'>=</span> <span class='fl'>3</span>), <span class='no'>letters</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='kw'>sep</span> <span class='kw'>=</span> <span class='st'>"_"</span>)
+)
+
+<span class='co'># This is just one example of an evaluation using the kinetic model used for</span>
+<span class='co'># the generation of the data</span>
+<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_SFO_lin</span>, <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>,
+ <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='synthetic_data_for_UBA_2014-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.49.6
+#&gt; R version used for fitting: 3.6.1
+#&gt; Date of fit: Thu Sep 19 12:43:02 2019
+#&gt; Date of summary: Thu Sep 19 12:43:02 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_parent/dt = - k_parent * parent
+#&gt; d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1
+#&gt; d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2
+#&gt;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted using 847 model solutions performed in 2.51 s
+#&gt;
+#&gt; Error model: Constant variance
+#&gt;
+#&gt; Error model algorithm: OLS
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 101.350000 state
+#&gt; k_parent 0.100000 deparm
+#&gt; k_M1 0.100100 deparm
+#&gt; k_M2 0.100200 deparm
+#&gt; f_parent_to_M1 0.500000 deparm
+#&gt; f_M1_to_M2 0.500000 deparm
+#&gt; sigma 2.273126 error
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 101.350000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_M1 -2.301586 -Inf Inf
+#&gt; log_k_M2 -2.300587 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt; f_M1_ilr_1 0.000000 -Inf Inf
+#&gt; sigma 2.273126 0 Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; M1_0 0 state
+#&gt; M2_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 102.1000 1.57000 98.8600 105.3000
+#&gt; log_k_parent -0.3020 0.03885 -0.3812 -0.2229
+#&gt; log_k_M1 -1.2070 0.07123 -1.3520 -1.0620
+#&gt; log_k_M2 -3.9010 0.06571 -4.0350 -3.7670
+#&gt; f_parent_ilr_1 0.8492 0.16640 0.5103 1.1880
+#&gt; f_M1_ilr_1 0.6780 0.17600 0.3196 1.0360
+#&gt; sigma 2.2730 0.25740 1.7490 2.7970
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_ilr_1
+#&gt; parent_0 1.000e+00 3.933e-01 -1.605e-01 2.819e-02 -4.624e-01
+#&gt; log_k_parent 3.933e-01 1.000e+00 -4.082e-01 7.166e-02 -5.682e-01
+#&gt; log_k_M1 -1.605e-01 -4.082e-01 1.000e+00 -3.929e-01 7.478e-01
+#&gt; log_k_M2 2.819e-02 7.166e-02 -3.929e-01 1.000e+00 -2.658e-01
+#&gt; f_parent_ilr_1 -4.624e-01 -5.682e-01 7.478e-01 -2.658e-01 1.000e+00
+#&gt; f_M1_ilr_1 1.614e-01 4.102e-01 -8.109e-01 5.419e-01 -8.605e-01
+#&gt; sigma -3.704e-09 -1.104e-08 5.922e-08 -3.673e-08 5.867e-08
+#&gt; f_M1_ilr_1 sigma
+#&gt; parent_0 1.614e-01 -3.704e-09
+#&gt; log_k_parent 4.102e-01 -1.104e-08
+#&gt; log_k_M1 -8.109e-01 5.922e-08
+#&gt; log_k_M2 5.419e-01 -3.673e-08
+#&gt; f_parent_ilr_1 -8.605e-01 5.867e-08
+#&gt; f_M1_ilr_1 1.000e+00 -8.075e-08
+#&gt; sigma -8.075e-08 1.000e+00
+#&gt;
+#&gt; Backtransformed parameters:
+#&gt; Confidence intervals for internally transformed parameters are asymmetric.
+#&gt; t-test (unrealistically) based on the assumption of normal distribution
+#&gt; for estimators of untransformed parameters.
+#&gt; Estimate t value Pr(&gt;t) Lower Upper
+#&gt; parent_0 102.10000 65.000 7.281e-36 98.86000 105.30000
+#&gt; k_parent 0.73930 25.740 2.948e-23 0.68310 0.80020
+#&gt; k_M1 0.29920 14.040 1.577e-15 0.25880 0.34590
+#&gt; k_M2 0.02023 15.220 1.653e-16 0.01769 0.02312
+#&gt; f_parent_to_M1 0.76870 18.370 7.295e-19 0.67300 0.84290
+#&gt; f_M1_to_M2 0.72290 14.500 6.418e-16 0.61110 0.81240
+#&gt; sigma 2.27300 8.832 2.161e-10 1.74900 2.79700
+#&gt;
+#&gt; FOCUS Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 8.454 6 17
+#&gt; parent 8.660 2 6
+#&gt; M1 10.583 2 5
+#&gt; M2 3.586 2 6
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_M1 0.7687
+#&gt; parent_sink 0.2313
+#&gt; M1_M2 0.7229
+#&gt; M1_sink 0.2771
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 0.9376 3.114
+#&gt; M1 2.3170 7.697
+#&gt; M2 34.2689 113.839
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual
+#&gt; 0 parent 101.5 1.021e+02 -0.56248
+#&gt; 0 parent 101.2 1.021e+02 -0.86248
+#&gt; 1 parent 53.9 4.873e+01 5.17118
+#&gt; 1 parent 47.5 4.873e+01 -1.22882
+#&gt; 3 parent 10.4 1.111e+01 -0.70773
+#&gt; 3 parent 7.6 1.111e+01 -3.50773
+#&gt; 7 parent 1.1 5.772e-01 0.52283
+#&gt; 7 parent 0.3 5.772e-01 -0.27717
+#&gt; 14 parent 3.5 3.264e-03 3.49674
+#&gt; 28 parent 3.2 1.045e-07 3.20000
+#&gt; 90 parent 0.6 -1.875e-11 0.60000
+#&gt; 120 parent 3.5 -2.805e-11 3.50000
+#&gt; 1 M1 36.4 3.479e+01 1.61088
+#&gt; 1 M1 37.4 3.479e+01 2.61088
+#&gt; 3 M1 34.3 3.937e+01 -5.07027
+#&gt; 3 M1 39.8 3.937e+01 0.42973
+#&gt; 7 M1 15.1 1.549e+01 -0.38715
+#&gt; 7 M1 17.8 1.549e+01 2.31285
+#&gt; 14 M1 5.8 1.995e+00 3.80469
+#&gt; 14 M1 1.2 1.995e+00 -0.79531
+#&gt; 60 M1 0.5 2.111e-06 0.50000
+#&gt; 90 M1 3.2 2.913e-10 3.20000
+#&gt; 120 M1 1.5 3.625e-11 1.50000
+#&gt; 120 M1 0.6 3.625e-11 0.60000
+#&gt; 1 M2 4.8 4.455e+00 0.34517
+#&gt; 3 M2 20.9 2.153e+01 -0.62527
+#&gt; 3 M2 19.3 2.153e+01 -2.22527
+#&gt; 7 M2 42.0 4.192e+01 0.07941
+#&gt; 7 M2 43.1 4.192e+01 1.17941
+#&gt; 14 M2 49.4 4.557e+01 3.83353
+#&gt; 14 M2 44.3 4.557e+01 -1.26647
+#&gt; 28 M2 34.6 3.547e+01 -0.87275
+#&gt; 28 M2 33.0 3.547e+01 -2.47275
+#&gt; 60 M2 18.8 1.858e+01 0.21837
+#&gt; 60 M2 17.6 1.858e+01 -0.98163
+#&gt; 90 M2 10.6 1.013e+01 0.47130
+#&gt; 90 M2 10.8 1.013e+01 0.67130
+#&gt; 120 M2 9.8 5.521e+00 4.27893
+#&gt; 120 M2 3.3 5.521e+00 -2.22107</div></pre>
+ </div>
+ <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+ <h2>Contents</h2>
+ <ul class="nav nav-pills nav-stacked">
+
+ <li><a href="#format">Format</a></li>
+
+ <li><a href="#source">Source</a></li>
+
+ <li><a href="#examples">Examples</a></li>
+ </ul>
+
+ </div>
+</div>
+
+
+ <footer>
+ <div class="copyright">
+ <p>Developed by Johannes Ranke.</p>
+</div>
+
+<div class="pkgdown">
+ <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.4.1.</p>
+</div>
+
+ </footer>
+ </div>
+
+
+
+
+ </body>
+</html>
+
+
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index e75a0097..00d1a840 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -148,7 +148,7 @@
<loc>https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html</loc>
</url>
<url>
- <loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA.html</loc>
+ <loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html</loc>
</url>
<url>
<loc>https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html</loc>
diff --git a/man/synthetic_data_for_UBA_2014.Rd b/man/synthetic_data_for_UBA_2014.Rd
index 4e10d209..2d726d5a 100644
--- a/man/synthetic_data_for_UBA_2014.Rd
+++ b/man/synthetic_data_for_UBA_2014.Rd
@@ -1,146 +1,146 @@
-\name{synthetic_data_for_UBA_2014}
-\alias{synthetic_data_for_UBA_2014}
-\docType{data}
-\title{
- Synthetic datasets for one parent compound with two metabolites
-}
-\description{
- The 12 datasets were generated using four different models and three different
- variance components. The four models are either the SFO or the DFOP model with either
- two sequential or two parallel metabolites.
-
- Variance component 'a' is based on a normal distribution with standard deviation of 3,
- Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
- Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
- minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
- for the increase of the standard deviation with y. Note that this is a simplified version
- of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
- measured values approximates lognormal distribution for high values, whereas we are using
- normally distributed error components all along.
-
- Initial concentrations for metabolites and all values where adding the variance component resulted
- in a value below the assumed limit of detection of 0.1 were set to \code{NA}.
-
- As an example, the first dataset has the title \code{SFO_lin_a} and is based on the SFO model
- with two sequential metabolites (linear pathway), with added variance component 'a'.
-
- Compare also the code in the example section to see the degradation models.
-}
-\usage{synthetic_data_for_UBA_2014}
-\format{
- A list containing twelve datasets as an R6 class defined by \code{\link{mkinds}},
- each containing, among others, the following components
- \describe{
- \item{\code{title}}{The name of the dataset, e.g. \code{SFO_lin_a}}
- \item{\code{data}}{A data frame with the data in the form expected by \code{\link{mkinfit}}}
- }
-}
-\source{
- Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
- zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452
-
- Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
- measurement error in analytical chemistry. Technometrics 37(2), 176-184.
-}
-\examples{
-# 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")
-
-
-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")
-
-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")
-
-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")
-
-# 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$time == 0 & match(d_rep$name, 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)
-}
-\keyword{datasets}
+\name{synthetic_data_for_UBA_2014}
+\alias{synthetic_data_for_UBA_2014}
+\docType{data}
+\title{
+ Synthetic datasets for one parent compound with two metabolites
+}
+\description{
+ The 12 datasets were generated using four different models and three different
+ variance components. The four models are either the SFO or the DFOP model with either
+ two sequential or two parallel metabolites.
+
+ Variance component 'a' is based on a normal distribution with standard deviation of 3,
+ Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
+ Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
+ minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
+ for the increase of the standard deviation with y. Note that this is a simplified version
+ of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
+ measured values approximates lognormal distribution for high values, whereas we are using
+ normally distributed error components all along.
+
+ Initial concentrations for metabolites and all values where adding the variance component resulted
+ in a value below the assumed limit of detection of 0.1 were set to \code{NA}.
+
+ As an example, the first dataset has the title \code{SFO_lin_a} and is based on the SFO model
+ with two sequential metabolites (linear pathway), with added variance component 'a'.
+
+ Compare also the code in the example section to see the degradation models.
+}
+\usage{synthetic_data_for_UBA_2014}
+\format{
+ A list containing twelve datasets as an R6 class defined by \code{\link{mkinds}},
+ each containing, among others, the following components
+ \describe{
+ \item{\code{title}}{The name of the dataset, e.g. \code{SFO_lin_a}}
+ \item{\code{data}}{A data frame with the data in the form expected by \code{\link{mkinfit}}}
+ }
+}
+\source{
+ Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
+ zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452
+
+ Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
+ measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+}
+\examples{
+# 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")
+
+
+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")
+
+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")
+
+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")
+
+# 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 & match(d_rep$name, c("M1", "M2"), "value"] <- 0
+# d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))
+# d_NA$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)
+}
+\keyword{datasets}
diff --git a/vignettes/mkin_benchmarks.rda b/vignettes/mkin_benchmarks.rda
index aa794a02..bf9aec62 100644
--- a/vignettes/mkin_benchmarks.rda
+++ b/vignettes/mkin_benchmarks.rda
Binary files differ

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