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authorJohannes Ranke <jranke@uni-bremen.de>2019-01-31 16:55:20 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2019-01-31 16:55:20 +0100
commit3eefecf0adfbb30b8fb895c244dea6903bcb3e9c (patch)
treebb70432fd6f13ca306443047d6911a797757ee96
parente5453f4d020aa120cabea074120f587f8ea4735f (diff)
Restore NAMESPACE
which was accidentally overwritten by pkgdown -> roxygen
-rw-r--r--NAMESPACE32
-rw-r--r--R/mkinds.R17
-rw-r--r--docs/articles/FOCUS_D.html14
-rw-r--r--docs/articles/FOCUS_L.html40
-rw-r--r--docs/articles/web_only/compiled_models.html10
-rw-r--r--docs/reference/AIC.mmkin.html16
-rw-r--r--docs/reference/DFOP.solution.html2
-rw-r--r--docs/reference/Extract.mmkin.html402
-rw-r--r--docs/reference/FOMC.solution.html2
-rw-r--r--docs/reference/HS.solution.html2
-rw-r--r--docs/reference/IORE.solution.html15
-rw-r--r--docs/reference/SFO.solution.html2
-rw-r--r--docs/reference/SFORB.solution.html2
-rw-r--r--docs/reference/add_err.html20
-rw-r--r--docs/reference/endpoints.html12
-rw-r--r--docs/reference/geometric_mean.html2
-rw-r--r--docs/reference/ilr.html15
-rw-r--r--docs/reference/logLik.mkinfit.html14
-rw-r--r--docs/reference/max_twa_parent.html4
-rw-r--r--docs/reference/mccall81_245T.html183
-rw-r--r--docs/reference/mkin_long_to_wide.html24
-rw-r--r--docs/reference/mkin_wide_to_long.html8
-rw-r--r--docs/reference/mkinerrmin.html12
-rw-r--r--docs/reference/mkinfit.html906
-rw-r--r--docs/reference/mkinmod.html35
-rw-r--r--docs/reference/mkinparplot.html3
-rw-r--r--docs/reference/mkinpredict.html165
-rw-r--r--docs/reference/mkinresplot.html3
-rw-r--r--docs/reference/mkinsub.html6
-rw-r--r--docs/reference/mmkin.html36
-rw-r--r--docs/reference/plot.mkinfit.html9
-rw-r--r--docs/reference/plot.mmkin.html5
-rw-r--r--docs/reference/print.mkinmod.html18
-rw-r--r--docs/reference/schaefer07_complex_case.html20
-rw-r--r--docs/reference/summary.mkinfit.html71
-rw-r--r--docs/reference/test_data_from_UBA_2014.html48
-rw-r--r--docs/reference/transform_odeparms.html316
-rw-r--r--inst/examples/mkinds.R1
-rw-r--r--vignettes/FOCUS_D.Rmd11
-rw-r--r--vignettes/FOCUS_D.html438
40 files changed, 2710 insertions, 231 deletions
diff --git a/NAMESPACE b/NAMESPACE
index 3286b2b8..fc812f46 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,29 @@
-# Generated by roxygen2: do not edit by hand
+# Export all names
+exportPattern("^[^\\.]")
+S3method(print, mkinds)
+S3method(print, mkinmod)
+S3method(plot, mkinfit)
+S3method(summary, mkinfit)
+S3method(print, summary.mkinfit)
+S3method(logLik, mkinfit)
+S3method(plot, mmkin)
+S3method("[", mmkin)
+S3method(AIC, mmkin)
+S3method(mkinpredict, mkinmod)
+S3method(mkinpredict, mkinfit)
-S3method(print,mkinds)
-export(mkinds)
-importFrom(R6,R6Class)
+import(
+ stats,
+ graphics,
+ FME,
+ minpack.lm,
+ rootSolve,
+ inline,
+ parallel
+)
+importFrom(deSolve, ode)
+importFrom(methods, signature)
+importFrom(R6, R6Class)
+importFrom(grDevices, dev.cur)
+importFrom(plyr, join)
+importFrom(utils, write.table)
diff --git a/R/mkinds.R b/R/mkinds.R
index bed49be3..257a17c4 100644
--- a/R/mkinds.R
+++ b/R/mkinds.R
@@ -1,4 +1,4 @@
-# Copyright (C) 2015,2018 Johannes Ranke
+# Copyright (C) 2015,2018,2019 Johannes Ranke
# Contact: jranke@uni-bremen.de
# This file is part of the R package mkin
@@ -16,21 +16,6 @@
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>
-#' A dataset class for mkin
-#'
-#' @docType class
-#' @importFrom R6 R6Class
-#' @export
-#' @format An \code{\link{R6Class}} generator object.
-#' @field title A full title for the dataset
-#' @field sampling times The sampling times
-#' @field time_unit The time unit
-#' @field observed Names of the observed compounds
-#' @field unit The unit of the observations
-#' @field replicates The number of replicates
-#' @field data A dataframe with at least the columns name, time and value
-#' in order to be compatible with mkinfit
-#' @example inst/examples/mkinds.R
mkinds <- R6Class("mkinds",
public = list(
title = NULL,
diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html
index deab980c..9acab53a 100644
--- a/docs/articles/FOCUS_D.html
+++ b/docs/articles/FOCUS_D.html
@@ -94,8 +94,8 @@
-<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)</a>
+<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look at the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(mkin, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(FOCUS_<span class="dv">2006</span>_D)</a></code></pre></div>
<pre><code>## name time value
## 1 parent 0 99.46
@@ -161,10 +161,10 @@
<p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p>
<p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(fit)</a></code></pre></div>
-<pre><code>## mkin version used for fitting: 0.9.47.6
-## R version used for fitting: 3.5.1
-## Date of fit: Fri Nov 23 19:58:46 2018
-## Date of summary: Fri Nov 23 19:58:47 2018
+<pre><code>## mkin version used for fitting: 0.9.47.5
+## R version used for fitting: 3.5.2
+## Date of fit: Thu Jan 31 16:52:37 2019
+## Date of summary: Thu Jan 31 16:52:38 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -172,7 +172,7 @@
##
## Model predictions using solution type deSolve
##
-## Fitted with method Port using 153 model solutions performed in 0.736 s
+## Fitted with method Port using 153 model solutions performed in 0.695 s
##
## Weighting: none
##
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index b53ab0d0..e3ca9aff 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -111,8 +111,8 @@
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(m.L1.SFO)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:40 2019
-## Date of summary: Thu Jan 31 15:43:40 2019
+## Date of fit: Thu Jan 31 16:52:39 2019
+## Date of summary: Thu Jan 31 16:52:39 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent
@@ -202,15 +202,15 @@
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(m.L1.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:42 2019
-## Date of summary: Thu Jan 31 15:43:42 2019
+## Date of fit: Thu Jan 31 16:52:41 2019
+## Date of summary: Thu Jan 31 16:52:41 2019
##
## Equations:
## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
##
## Model predictions using solution type analytical
##
-## Fitted with method Port using 611 model solutions performed in 1.502 s
+## Fitted with method Port using 611 model solutions performed in 1.49 s
##
## Weighting: none
##
@@ -297,15 +297,15 @@
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">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: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:42 2019
-## Date of summary: Thu Jan 31 15:43:42 2019
+## Date of fit: Thu Jan 31 16:52:42 2019
+## Date of summary: Thu Jan 31 16:52:42 2019
##
## Equations:
## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
##
## Model predictions using solution type analytical
##
-## Fitted with method Port using 81 model solutions performed in 0.195 s
+## Fitted with method Port using 81 model solutions performed in 0.198 s
##
## Weighting: none
##
@@ -368,8 +368,8 @@
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">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: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:43 2019
-## Date of summary: Thu Jan 31 15:43:43 2019
+## Date of fit: Thu Jan 31 16:52:43 2019
+## Date of summary: Thu Jan 31 16:52:43 2019
##
## Equations:
## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -378,7 +378,7 @@
##
## Model predictions using solution type analytical
##
-## Fitted with method Port using 336 model solutions performed in 0.836 s
+## Fitted with method Port using 336 model solutions performed in 0.839 s
##
## Weighting: none
##
@@ -460,8 +460,8 @@
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">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: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:44 2019
-## Date of summary: Thu Jan 31 15:43:45 2019
+## Date of fit: Thu Jan 31 16:52:44 2019
+## Date of summary: Thu Jan 31 16:52:44 2019
##
## Equations:
## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -470,7 +470,7 @@
##
## Model predictions using solution type analytical
##
-## Fitted with method Port using 137 model solutions performed in 0.34 s
+## Fitted with method Port using 137 model solutions performed in 0.341 s
##
## Weighting: none
##
@@ -561,15 +561,15 @@
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">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: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:45 2019
-## Date of summary: Thu Jan 31 15:43:45 2019
+## Date of fit: Thu Jan 31 16:52:44 2019
+## Date of summary: Thu Jan 31 16:52:45 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent
##
## Model predictions using solution type analytical
##
-## Fitted with method Port using 46 model solutions performed in 0.109 s
+## Fitted with method Port using 46 model solutions performed in 0.11 s
##
## Weighting: none
##
@@ -621,15 +621,15 @@
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb28-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">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: 0.9.47.5
## R version used for fitting: 3.5.2
-## Date of fit: Thu Jan 31 15:43:45 2019
-## Date of summary: Thu Jan 31 15:43:45 2019
+## Date of fit: Thu Jan 31 16:52:45 2019
+## Date of summary: Thu Jan 31 16:52:45 2019
##
## Equations:
## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
##
## Model predictions using solution type analytical
##
-## Fitted with method Port using 66 model solutions performed in 0.163 s
+## Fitted with method Port using 66 model solutions performed in 0.162 s
##
## Weighting: none
##
diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html
index d7d76b49..08bb9b44 100644
--- a/docs/articles/web_only/compiled_models.html
+++ b/docs/articles/web_only/compiled_models.html
@@ -126,9 +126,9 @@
<a class="sourceLine" id="cb5-16" data-line-number="16">}</a></code></pre></div>
<pre><code>## Lade nötiges Paket: rbenchmark</code></pre>
<pre><code>## test replications elapsed relative user.self sys.self
-## 3 deSolve, compiled 3 2.331 1.000 2.330 0
-## 1 deSolve, not compiled 3 17.369 7.451 17.360 0
-## 2 Eigenvalue based 3 2.878 1.235 2.876 0
+## 3 deSolve, compiled 3 2.353 1.000 2.352 0
+## 1 deSolve, not compiled 3 17.619 7.488 17.609 0
+## 2 Eigenvalue based 3 2.899 1.232 2.898 0
## user.child sys.child
## 3 0 0
## 1 0 0
@@ -157,8 +157,8 @@
<a class="sourceLine" id="cb8-16" data-line-number="16">}</a></code></pre></div>
<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
<pre><code>## test replications elapsed relative user.self sys.self
-## 2 deSolve, compiled 3 4.120 1.000 4.116 0
-## 1 deSolve, not compiled 3 37.011 8.983 36.993 0
+## 2 deSolve, compiled 3 4.180 1.000 4.177 0
+## 1 deSolve, not compiled 3 37.331 8.931 37.312 0
## user.child sys.child
## 2 0 0
## 1 0 0</code></pre>
diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html
index a90e81bb..d90b325f 100644
--- a/docs/reference/AIC.mmkin.html
+++ b/docs/reference/AIC.mmkin.html
@@ -160,12 +160,22 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <span class='no'>f</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span>),
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='st'>"FOCUS A"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_A</span>,
- <span class='st'>"FOCUS C"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mmkin(c("SFO", "FOMC", "DFOP"), list(`FOCUS A` = FOCUS_2006_A, `FOCUS C` = FOCUS_2006_C)): konnte Funktion "mmkin" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[<span class='fl'>1</span>, <span class='st'>"FOCUS A"</span>]) <span class='co'># We get a single number for a single fit</span></div><div class='output co'>#&gt; <span class='error'>Error in AIC(f[1, "FOCUS A"]): Objekt 'f' nicht gefunden</span></div><div class='input'>
+ <span class='st'>"FOCUS C"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>))
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[<span class='fl'>1</span>, <span class='st'>"FOCUS A"</span>]) <span class='co'># We get a single number for a single fit</span></div><div class='output co'>#&gt; [1] 55.32452</div><div class='input'>
<span class='co'># For FOCUS A, the models fit almost equally well, so the higher the number</span>
<span class='co'># of parameters, the higher (worse) the AIC</span>
- <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>])</div><div class='output co'>#&gt; <span class='error'>Error in AIC(f[, "FOCUS A"]): Objekt 'f' nicht gefunden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>], <span class='kw'>k</span> <span class='kw'>=</span> <span class='fl'>0</span>) <span class='co'># If we do not penalize additional parameters, we get nearly the same</span></div><div class='output co'>#&gt; <span class='error'>Error in AIC(f[, "FOCUS A"], k = 0): Objekt 'f' nicht gefunden</span></div><div class='input'>
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>])</div><div class='output co'>#&gt; df AIC
+#&gt; SFO 3 55.32452
+#&gt; FOMC 4 57.32477
+#&gt; DFOP 5 59.32452</div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>], <span class='kw'>k</span> <span class='kw'>=</span> <span class='fl'>0</span>) <span class='co'># If we do not penalize additional parameters, we get nearly the same</span></div><div class='output co'>#&gt; df AIC
+#&gt; SFO 3 49.32452
+#&gt; FOMC 4 49.32477
+#&gt; DFOP 5 49.32452</div><div class='input'>
<span class='co'># For FOCUS C, the more complex models fit better</span>
- <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='output co'>#&gt; <span class='error'>Error in AIC(f[, "FOCUS C"]): Objekt 'f' nicht gefunden</span></div></pre>
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='output co'>#&gt; df AIC
+#&gt; SFO 3 59.84675
+#&gt; FOMC 4 44.70584
+#&gt; DFOP 5 29.08369</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html
index c82eb161..7f17e421 100644
--- a/docs/reference/DFOP.solution.html
+++ b/docs/reference/DFOP.solution.html
@@ -173,7 +173,7 @@
<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://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>DFOP.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.3</span>), <span class='fl'>0</span>, <span class='fl'>4</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='output co'>#&gt; <span class='error'>Error in DFOP.solution(x, 100, 5, 0.5, 0.3): konnte Funktion "DFOP.solution" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>DFOP.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.3</span>), <span class='fl'>0</span>, <span class='fl'>4</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='img'><img src='DFOP.solution-1.png' alt='' width='700' height='433' /></div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html
index 1be9e0f1..2a1d63b5 100644
--- a/docs/reference/Extract.mmkin.html
+++ b/docs/reference/Extract.mmkin.html
@@ -166,15 +166,411 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <span class='co'># Only use one core, to pass R CMD check --as-cran</span>
<span class='no'>fits</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>B</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_B</span>, <span class='kw'>C</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</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>)</div><div class='output co'>#&gt; <span class='error'>Error in mmkin(c("SFO", "FOMC"), list(B = FOCUS_2006_B, C = FOCUS_2006_C), cores = 1, quiet = TRUE): konnte Funktion "mmkin" nicht finden</span></div><div class='input'> <span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ]</div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'fits' nicht gefunden</span></div><div class='input'> <span class='no'>fits</span>[, <span class='st'>"B"</span>]</div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'fits' nicht gefunden</span></div><div class='input'> <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'fits' nicht gefunden</span></div><div class='input'>
+ <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'>fits</span>[<span class='st'>"FOMC"</span>, ]</div><div class='output co'>#&gt; dataset
+#&gt; model B C
+#&gt; FOMC List,45 List,45
+#&gt; attr(,"class")
+#&gt; [1] "mmkin"</div><div class='input'> <span class='no'>fits</span>[, <span class='st'>"B"</span>]</div><div class='output co'>#&gt; dataset
+#&gt; model B
+#&gt; SFO List,45
+#&gt; FOMC List,45
+#&gt; attr(,"class")
+#&gt; [1] "mmkin"</div><div class='input'> <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#&gt; dataset
+#&gt; model B
+#&gt; SFO List,45
+#&gt; attr(,"class")
+#&gt; [1] "mmkin"</div><div class='input'>
<span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/head'>head</a></span>(
<span class='co'># This extracts an mkinfit object with lots of components</span>
<span class='no'>fits</span><span class='kw'>[[</span><span class='st'>"FOMC"</span>, <span class='st'>"B"</span>]]
- )</div><div class='output co'>#&gt; <span class='error'>Error in head(fits[["FOMC", "B"]]): Objekt 'fits' nicht gefunden</span></div><div class='input'>
+ )</div><div class='output co'>#&gt; $par
+#&gt; parent_0 log_alpha log_beta
+#&gt; 99.666193 2.549849 5.050586
+#&gt;
+#&gt; $ssr
+#&gt; [1] 28.58291
+#&gt;
+#&gt; $convergence
+#&gt; [1] 0
+#&gt;
+#&gt; $iterations
+#&gt; [1] 21
+#&gt;
+#&gt; $evaluations
+#&gt; function gradient
+#&gt; 25 78
+#&gt;
+#&gt; $counts
+#&gt; [1] "relative convergence (4)"
+#&gt; </div><div class='input'>
<span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/head'>head</a></span>(
<span class='co'># The same can be achieved by</span>
<span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>, <span class='kw'>drop</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>]
- )</div><div class='output co'>#&gt; <span class='error'>Error in head(fits["SFO", "B", drop = TRUE]): Objekt 'fits' nicht gefunden</span></div></pre>
+ )</div><div class='output co'>#&gt; [[1]]
+#&gt; $par
+#&gt; parent_0 log_k_parent_sink
+#&gt; 99.174072 -2.549028
+#&gt;
+#&gt; $ssr
+#&gt; [1] 30.65564
+#&gt;
+#&gt; $convergence
+#&gt; [1] 0
+#&gt;
+#&gt; $iterations
+#&gt; [1] 5
+#&gt;
+#&gt; $evaluations
+#&gt; function gradient
+#&gt; 8 15
+#&gt;
+#&gt; $counts
+#&gt; [1] "relative convergence (4)"
+#&gt;
+#&gt; $hessian
+#&gt; parent_0 log_k_parent_sink
+#&gt; parent_0 4.163631 -94.09343
+#&gt; log_k_parent_sink -94.093431 6311.34610
+#&gt;
+#&gt; $residuals
+#&gt; parent parent parent parent parent parent
+#&gt; 0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730
+#&gt; parent parent
+#&gt; -0.24260405 -0.07020339
+#&gt;
+#&gt; $ms
+#&gt; [1] 3.831956
+#&gt;
+#&gt; $var_ms
+#&gt; parent
+#&gt; 3.831956
+#&gt;
+#&gt; $var_ms_unscaled
+#&gt; parent
+#&gt; 3.831956
+#&gt;
+#&gt; $var_ms_unweighted
+#&gt; parent
+#&gt; 3.831956
+#&gt;
+#&gt; $rank
+#&gt; [1] 2
+#&gt;
+#&gt; $df.residual
+#&gt; [1] 6
+#&gt;
+#&gt; $solution_type
+#&gt; [1] "analytical"
+#&gt;
+#&gt; $transform_rates
+#&gt; [1] TRUE
+#&gt;
+#&gt; $transform_fractions
+#&gt; [1] TRUE
+#&gt;
+#&gt; $method.modFit
+#&gt; [1] "Port"
+#&gt;
+#&gt; $maxit.modFit
+#&gt; [1] "auto"
+#&gt;
+#&gt; $calls
+#&gt; [1] 29
+#&gt;
+#&gt; $time
+#&gt; User System verstrichen
+#&gt; 0.197 0.000 0.198
+#&gt;
+#&gt; $mkinmod
+#&gt; &lt;mkinmod&gt; model generated with
+#&gt; Use of formation fractions $use_of_ff: min
+#&gt; Specification $spec:
+#&gt; $parent
+#&gt; $type: SFO; $sink: TRUE
+#&gt; Coefficient matrix $coefmat available
+#&gt; Differential equations:
+#&gt; d_parent/dt = - k_parent_sink * parent
+#&gt;
+#&gt; $observed
+#&gt; name time value
+#&gt; 1 parent 0 98.62
+#&gt; 2 parent 3 81.43
+#&gt; 3 parent 7 53.18
+#&gt; 4 parent 14 34.89
+#&gt; 5 parent 30 10.09
+#&gt; 6 parent 62 1.50
+#&gt; 7 parent 90 0.33
+#&gt; 8 parent 118 0.08
+#&gt;
+#&gt; $obs_vars
+#&gt; [1] "parent"
+#&gt;
+#&gt; $predicted
+#&gt; name time value
+#&gt; 1 parent 0.000000 99.17407218
+#&gt; 2 parent 1.191919 90.35253561
+#&gt; 3 parent 2.383838 82.31567498
+#&gt; 4 parent 3.000000 78.44547872
+#&gt; 5 parent 3.575758 74.99369333
+#&gt; 6 parent 4.767677 68.32300215
+#&gt; 7 parent 5.959596 62.24566915
+#&gt; 8 parent 7.000000 57.38445742
+#&gt; 9 parent 7.151515 56.70891509
+#&gt; 10 parent 8.343434 51.66465547
+#&gt; 11 parent 9.535354 47.06908288
+#&gt; 12 parent 10.727273 42.88228661
+#&gt; 13 parent 11.919192 39.06790599
+#&gt; 14 parent 13.111111 35.59281463
+#&gt; 15 parent 14.000000 33.20400061
+#&gt; 16 parent 14.303030 32.42683275
+#&gt; 17 parent 15.494949 29.54246504
+#&gt; 18 parent 16.686869 26.91466193
+#&gt; 19 parent 17.878788 24.52060198
+#&gt; 20 parent 19.070707 22.33949373
+#&gt; 21 parent 20.262626 20.35239512
+#&gt; 22 parent 21.454545 18.54204899
+#&gt; 23 parent 22.646465 16.89273320
+#&gt; 24 parent 23.838384 15.39012410
+#&gt; 25 parent 25.030303 14.02117212
+#&gt; 26 parent 26.222222 12.77398846
+#&gt; 27 parent 27.414141 11.63774182
+#&gt; 28 parent 28.606061 10.60256435
+#&gt; 29 parent 29.797980 9.65946594
+#&gt; 30 parent 30.000000 9.50814643
+#&gt; 31 parent 30.989899 8.80025617
+#&gt; 32 parent 32.181818 8.01747313
+#&gt; 33 parent 33.373737 7.30431867
+#&gt; 34 parent 34.565657 6.65459931
+#&gt; 35 parent 35.757576 6.06267251
+#&gt; 36 parent 36.949495 5.52339762
+#&gt; 37 parent 38.141414 5.03209124
+#&gt; 38 parent 39.333333 4.58448658
+#&gt; 39 parent 40.525253 4.17669637
+#&gt; 40 parent 41.717172 3.80517911
+#&gt; 41 parent 42.909091 3.46670832
+#&gt; 42 parent 44.101010 3.15834451
+#&gt; 43 parent 45.292929 2.87740968
+#&gt; 44 parent 46.484848 2.62146400
+#&gt; 45 parent 47.676768 2.38828471
+#&gt; 46 parent 48.868687 2.17584671
+#&gt; 47 parent 50.060606 1.98230508
+#&gt; 48 parent 51.252525 1.80597899
+#&gt; 49 parent 52.444444 1.64533711
+#&gt; 50 parent 53.636364 1.49898432
+#&gt; 51 parent 54.828283 1.36564963
+#&gt; 52 parent 56.020202 1.24417505
+#&gt; 53 parent 57.212121 1.13350565
+#&gt; 54 parent 58.404040 1.03268029
+#&gt; 55 parent 59.595960 0.94082335
+#&gt; 56 parent 60.787879 0.85713708
+#&gt; 57 parent 61.979798 0.78089471
+#&gt; 58 parent 62.000000 0.77966270
+#&gt; 59 parent 63.171717 0.71143411
+#&gt; 60 parent 64.363636 0.64815202
+#&gt; 61 parent 65.555556 0.59049888
+#&gt; 62 parent 66.747475 0.53797399
+#&gt; 63 parent 67.939394 0.49012119
+#&gt; 64 parent 69.131313 0.44652489
+#&gt; 65 parent 70.323232 0.40680649
+#&gt; 66 parent 71.515152 0.37062104
+#&gt; 67 parent 72.707071 0.33765429
+#&gt; 68 parent 73.898990 0.30761993
+#&gt; 69 parent 75.090909 0.28025713
+#&gt; 70 parent 76.282828 0.25532825
+#&gt; 71 parent 77.474747 0.23261679
+#&gt; 72 parent 78.666667 0.21192552
+#&gt; 73 parent 79.858586 0.19307474
+#&gt; 74 parent 81.050505 0.17590074
+#&gt; 75 parent 82.242424 0.16025436
+#&gt; 76 parent 83.434343 0.14599973
+#&gt; 77 parent 84.626263 0.13301305
+#&gt; 78 parent 85.818182 0.12118154
+#&gt; 79 parent 87.010101 0.11040244
+#&gt; 80 parent 88.202020 0.10058214
+#&gt; 81 parent 89.393939 0.09163535
+#&gt; 82 parent 90.000000 0.08739595
+#&gt; 83 parent 90.585859 0.08348439
+#&gt; 84 parent 91.777778 0.07605845
+#&gt; 85 parent 92.969697 0.06929305
+#&gt; 86 parent 94.161616 0.06312943
+#&gt; 87 parent 95.353535 0.05751406
+#&gt; 88 parent 96.545455 0.05239819
+#&gt; 89 parent 97.737374 0.04773737
+#&gt; 90 parent 98.929293 0.04349113
+#&gt; 91 parent 100.121212 0.03962259
+#&gt; 92 parent 101.313131 0.03609816
+#&gt; 93 parent 102.505051 0.03288723
+#&gt; 94 parent 103.696970 0.02996191
+#&gt; 95 parent 104.888889 0.02729679
+#&gt; 96 parent 106.080808 0.02486874
+#&gt; 97 parent 107.272727 0.02265667
+#&gt; 98 parent 108.464646 0.02064136
+#&gt; 99 parent 109.656566 0.01880531
+#&gt; 100 parent 110.848485 0.01713257
+#&gt; 101 parent 112.040404 0.01560863
+#&gt; 102 parent 113.232323 0.01422024
+#&gt; 103 parent 114.424242 0.01295535
+#&gt; 104 parent 115.616162 0.01180297
+#&gt; 105 parent 116.808081 0.01075310
+#&gt; 106 parent 118.000000 0.00979661
+#&gt;
+#&gt; $cost
+#&gt; function (P)
+#&gt; {
+#&gt; assign("calls", calls + 1, inherits = TRUE)
+#&gt; if (trace_parms)
+#&gt; cat(P, "\n")
+#&gt; if (length(state.ini.optim) &gt; 0) {
+#&gt; odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
+#&gt; names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+#&gt; }
+#&gt; else {
+#&gt; odeini &lt;- state.ini.fixed
+#&gt; names(odeini) &lt;- state.ini.fixed.boxnames
+#&gt; }
+#&gt; odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)],
+#&gt; transparms.fixed)
+#&gt; parms &lt;- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates,
+#&gt; transform_fractions = transform_fractions)
+#&gt; out &lt;- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type,
+#&gt; use_compiled = use_compiled, method.ode = method.ode,
+#&gt; atol = atol, rtol = rtol, ...)
+#&gt; assign("out_predicted", out, inherits = TRUE)
+#&gt; mC &lt;- modCost(out, observed, y = "value", err = err, weight = weight,
+#&gt; scaleVar = scaleVar)
+#&gt; if (mC$model &lt; cost.old) {
+#&gt; if (!quiet)
+#&gt; cat("Model cost at call ", calls, ": ", mC$model,
+#&gt; "\n")
+#&gt; if (plot) {
+#&gt; outtimes_plot = seq(min(observed$time), max(observed$time),
+#&gt; length.out = 100)
+#&gt; out_plot &lt;- mkinpredict(mkinmod, parms, odeini, outtimes_plot,
+#&gt; solution_type = solution_type, use_compiled = use_compiled,
+#&gt; method.ode = method.ode, atol = atol, rtol = rtol,
+#&gt; ...)
+#&gt; plot(0, type = "n", xlim = range(observed$time),
+#&gt; ylim = c(0, max(observed$value, na.rm = TRUE)),
+#&gt; xlab = "Time", ylab = "Observed")
+#&gt; col_obs &lt;- pch_obs &lt;- 1:length(obs_vars)
+#&gt; lty_obs &lt;- rep(1, length(obs_vars))
+#&gt; names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars
+#&gt; for (obs_var in obs_vars) {
+#&gt; points(subset(observed, name == obs_var, c(time,
+#&gt; value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
+#&gt; }
+#&gt; matlines(out_plot$time, out_plot[-1], col = col_obs,
+#&gt; lty = lty_obs)
+#&gt; legend("topright", inset = c(0.05, 0.05), legend = obs_vars,
+#&gt; col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
+#&gt; }
+#&gt; assign("cost.old", mC$model, inherits = TRUE)
+#&gt; }
+#&gt; return(mC)
+#&gt; }
+#&gt; &lt;bytecode: 0x55555916c520&gt;
+#&gt; &lt;environment: 0x55555b68a808&gt;
+#&gt;
+#&gt; $cost_notrans
+#&gt; function (P)
+#&gt; {
+#&gt; if (length(state.ini.optim) &gt; 0) {
+#&gt; odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
+#&gt; names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
+#&gt; }
+#&gt; else {
+#&gt; odeini &lt;- state.ini.fixed
+#&gt; names(odeini) &lt;- state.ini.fixed.boxnames
+#&gt; }
+#&gt; odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)],
+#&gt; parms.fixed)
+#&gt; out &lt;- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type,
+#&gt; use_compiled = use_compiled, method.ode = method.ode,
+#&gt; atol = atol, rtol = rtol, ...)
+#&gt; mC &lt;- modCost(out, observed, y = "value", err = err, weight = weight,
+#&gt; scaleVar = scaleVar)
+#&gt; return(mC)
+#&gt; }
+#&gt; &lt;bytecode: 0x55555a1242b0&gt;
+#&gt; &lt;environment: 0x55555b68a808&gt;
+#&gt;
+#&gt; $hessian_notrans
+#&gt; parent_0 k_parent_sink
+#&gt; parent_0 4.163631 -1203.894
+#&gt; k_parent_sink -1203.893702 1033188.753
+#&gt;
+#&gt; $start
+#&gt; value type
+#&gt; parent_0 98.62 state
+#&gt; k_parent_sink 0.10 deparm
+#&gt;
+#&gt; $start_transformed
+#&gt; value lower upper
+#&gt; parent_0 98.620000 -Inf Inf
+#&gt; log_k_parent_sink -2.302585 -Inf Inf
+#&gt;
+#&gt; $fixed
+#&gt; [1] value type
+#&gt; &lt;0 Zeilen&gt; (oder row.names mit Länge 0)
+#&gt;
+#&gt; $data
+#&gt; time variable observed predicted residual
+#&gt; 1 0 parent 98.62 99.17407218 -0.55407218
+#&gt; 2 3 parent 81.43 78.44547872 2.98452128
+#&gt; 3 7 parent 53.18 57.38445742 -4.20445742
+#&gt; 4 14 parent 34.89 33.20400061 1.68599939
+#&gt; 5 30 parent 10.09 9.50814643 0.58185357
+#&gt; 6 62 parent 1.50 0.77966270 0.72033730
+#&gt; 7 90 parent 0.33 0.08739595 0.24260405
+#&gt; 8 118 parent 0.08 0.00979661 0.07020339
+#&gt;
+#&gt; $atol
+#&gt; [1] 1e-08
+#&gt;
+#&gt; $rtol
+#&gt; [1] 1e-10
+#&gt;
+#&gt; $weight.ini
+#&gt; [1] "none"
+#&gt;
+#&gt; $tc.ini
+#&gt; sigma_low rsd_high
+#&gt; 0.50 0.07
+#&gt;
+#&gt; $reweight.tol
+#&gt; [1] 1e-08
+#&gt;
+#&gt; $reweight.max.iter
+#&gt; [1] 10
+#&gt;
+#&gt; $bparms.optim
+#&gt; parent_0 k_parent_sink
+#&gt; 99.17407218 0.07815759
+#&gt;
+#&gt; $bparms.fixed
+#&gt; numeric(0)
+#&gt;
+#&gt; $bparms.ode
+#&gt; k_parent_sink
+#&gt; 0.07815759
+#&gt;
+#&gt; $bparms.state
+#&gt; parent
+#&gt; 99.17407
+#&gt;
+#&gt; $date
+#&gt; [1] "Thu Jan 31 16:50:45 2019"
+#&gt;
+#&gt; $version
+#&gt; [1] "0.9.47.6"
+#&gt;
+#&gt; $Rversion
+#&gt; [1] "3.5.2"
+#&gt;
+#&gt; attr(,"class")
+#&gt; [1] "mkinfit" "modFit"
+#&gt; </div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html
index bbe84618..3ece2c08 100644
--- a/docs/reference/FOMC.solution.html
+++ b/docs/reference/FOMC.solution.html
@@ -185,7 +185,7 @@ The form given here differs slightly from the original reference by Gustafson
<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://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>FOMC.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>10</span>, <span class='fl'>2</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='output co'>#&gt; <span class='error'>Error in FOMC.solution(x, 100, 10, 2): konnte Funktion "FOMC.solution" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>FOMC.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>10</span>, <span class='fl'>2</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='img'><img src='FOMC.solution-1.png' alt='' width='700' height='433' /></div></pre>
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<h2>Contents</h2>
diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html
index d836e8a5..0d8751e1 100644
--- a/docs/reference/HS.solution.html
+++ b/docs/reference/HS.solution.html
@@ -174,7 +174,7 @@
<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://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>HS.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>2</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='output co'>#&gt; <span class='error'>Error in HS.solution(x, 100, 2, 0.3, 0.5): konnte Funktion "HS.solution" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>HS.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>2</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='img'><img src='HS.solution-1.png' alt='' width='700' height='433' /></div></pre>
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<h2>Contents</h2>
diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html
index 5bc5a57d..2ad38a16 100644
--- a/docs/reference/IORE.solution.html
+++ b/docs/reference/IORE.solution.html
@@ -174,10 +174,19 @@
<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://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>IORE.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.2</span>, <span class='fl'>1.3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>,
- <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='output co'>#&gt; <span class='error'>Error in IORE.solution(x, 100, 0.2, 1.3): konnte Funktion "IORE.solution" nicht finden</span></div><div class='input'> <span class='no'>fit.fomc</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='no'>fit.iore</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit("IORE", FOCUS_2006_C, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='no'>fit.iore.deS</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'>
+ <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='img'><img src='IORE.solution-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='no'>fit.fomc</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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='no'>fit.iore</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='no'>fit.iore.deS</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/data.frame'>data.frame</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit.fomc</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit.iore</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit.iore.deS</span>),
- <span class='kw'>row.names</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"model par"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>)))</div><div class='output co'>#&gt; <span class='error'>Error in coef(fit.fomc): Objekt 'fit.fomc' nicht gefunden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/cbind'>rbind</a></span>(<span class='kw'>fomc</span> <span class='kw'>=</span> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.fomc</span>)$<span class='no'>distimes</span>, <span class='kw'>iore</span> <span class='kw'>=</span> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.iore</span>)$<span class='no'>distimes</span>,
- <span class='kw'>iore.deS</span> <span class='kw'>=</span> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.iore</span>)$<span class='no'>distimes</span>))</div><div class='output co'>#&gt; <span class='error'>Error in endpoints(fit.fomc): konnte Funktion "endpoints" nicht finden</span></div></pre>
+ <span class='kw'>row.names</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"model par"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>)))</div><div class='output co'>#&gt; coef.fit.fomc. coef.fit.iore. coef.fit.iore.deS.
+#&gt; model par 1 85.87489063 85.874890 85.874890
+#&gt; model par 2 0.05192238 -4.826631 -4.826631
+#&gt; model par 3 0.65096665 1.949403 1.949403</div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/cbind'>rbind</a></span>(<span class='kw'>fomc</span> <span class='kw'>=</span> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.fomc</span>)$<span class='no'>distimes</span>, <span class='kw'>iore</span> <span class='kw'>=</span> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.iore</span>)$<span class='no'>distimes</span>,
+ <span class='kw'>iore.deS</span> <span class='kw'>=</span> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.iore</span>)$<span class='no'>distimes</span>))</div><div class='output co'>#&gt; DT50 DT90 DT50back
+#&gt; fomc 1.785233 15.1479 4.559973
+#&gt; iore 1.785233 15.1479 4.559973
+#&gt; iore.deS 1.785233 15.1479 4.559973</div></pre>
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<h2>Contents</h2>
diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html
index 87ccdcf0..55ff216c 100644
--- a/docs/reference/SFO.solution.html
+++ b/docs/reference/SFO.solution.html
@@ -162,7 +162,7 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'> </div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFO.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><div class='output co'>#&gt; <span class='error'>Error in SFO.solution(x, 100, 3): konnte Funktion "SFO.solution" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> </div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFO.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><div class='img'><img src='SFO.solution-1.png' alt='' width='700' height='433' /></div></pre>
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<h2>Contents</h2>
diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html
index 49c78dc4..81ed61aa 100644
--- a/docs/reference/SFORB.solution.html
+++ b/docs/reference/SFORB.solution.html
@@ -179,7 +179,7 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'> </div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFORB.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><div class='output co'>#&gt; <span class='error'>Error in SFORB.solution(x, 100, 0.5, 2, 3): konnte Funktion "SFORB.solution" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> </div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFORB.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><div class='img'><img src='SFORB.solution-1.png' alt='' width='700' height='433' /></div></pre>
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<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html
index 78574e92..c8024d66 100644
--- a/docs/reference/add_err.html
+++ b/docs/reference/add_err.html
@@ -193,7 +193,7 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'># The kinetic model</span>
<span class='no'>m_SFO_SFO</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='error'>Error in mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <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='co'># Generate a prediction for a specific set of parameters</span>
<span class='no'>sampling_times</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>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>)
@@ -203,12 +203,15 @@
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.1</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='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Log'>log</a></span>(<span class='fl'>2</span>)/<span class='fl'>1000</span>),
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>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='no'>sampling_times</span>)</div><div class='output co'>#&gt; <span class='error'>Error in 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): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'>
+ <span class='no'>sampling_times</span>)
+
<span class='co'># Add an error term with a constant (independent of the value) standard deviation</span>
<span class='co'># of 10, and generate three datasets</span>
-<span class='no'>d_SFO_SFO_err</span> <span class='kw'>&lt;-</span> <span class='fu'>add_err</span>(<span class='no'>d_SFO_SFO</span>, <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fl'>10</span>, <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>seed</span> <span class='kw'>=</span> <span class='fl'>123456789</span> )</div><div class='output co'>#&gt; <span class='error'>Error in add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789): konnte Funktion "add_err" nicht finden</span></div><div class='input'>
+<span class='no'>d_SFO_SFO_err</span> <span class='kw'>&lt;-</span> <span class='fu'>add_err</span>(<span class='no'>d_SFO_SFO</span>, <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fl'>10</span>, <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>seed</span> <span class='kw'>=</span> <span class='fl'>123456789</span> )
+
<span class='co'># Name the datasets for nicer plotting</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>d_SFO_SFO_err</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>)</div><div class='output co'>#&gt; <span class='error'>Error in names(d_SFO_SFO_err) &lt;- paste("Dataset", 1:3): Objekt 'd_SFO_SFO_err' nicht gefunden</span></div><div class='input'>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>d_SFO_SFO_err</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>)
+
<span class='co'># Name the model in the list of models (with only one member in this case)</span>
<span class='co'># for nicer plotting later on.</span>
<span class='co'># Be quiet and use the faster Levenberg-Marquardt algorithm, as the datasets</span>
@@ -216,15 +219,16 @@
<span class='co'># checks</span>
<span class='no'>f_SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='st'>"SFO-SFO"</span> <span class='kw'>=</span> <span class='no'>m_SFO_SFO</span>),
<span class='no'>d_SFO_SFO_err</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='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mmkin(list(`SFO-SFO` = m_SFO_SFO), d_SFO_SFO_err, cores = 1, quiet = TRUE, method.modFit = "Marq"): konnte Funktion "mmkin" nicht finden</span></div><div class='input'>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>f_SFO_SFO</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(f_SFO_SFO): Objekt 'f_SFO_SFO' nicht gefunden</span></div><div class='input'>
+ <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)
+
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>f_SFO_SFO</span>)</div><div class='img'><img src='add_err-1.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># We would like to inspect the fit for dataset 3 more closely</span>
<span class='co'># Using double brackets makes the returned object an mkinfit object</span>
<span class='co'># instead of a list of mkinfit objects, so plot.mkinfit is used</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>f_SFO_SFO</span><span class='kw'>[[</span><span class='fl'>3</span>]], <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(f_SFO_SFO[[3]], show_residuals = TRUE): Objekt 'f_SFO_SFO' nicht gefunden</span></div><div class='input'>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>f_SFO_SFO</span><span class='kw'>[[</span><span class='fl'>3</span>]], <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='add_err-2.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># If we use single brackets, we should give two indices (model and dataset),</span>
<span class='co'># and plot.mmkin is used</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>f_SFO_SFO</span>[<span class='fl'>1</span>, <span class='fl'>3</span>])</div><div class='output co'>#&gt; <span class='error'>Error in plot(f_SFO_SFO[1, 3]): Objekt 'f_SFO_SFO' nicht gefunden</span></div><div class='input'>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>f_SFO_SFO</span>[<span class='fl'>1</span>, <span class='fl'>3</span>])</div><div class='img'><img src='add_err-3.png' alt='' width='700' height='433' /></div><div class='input'>
</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html
index 63db2467..9952f7ad 100644
--- a/docs/reference/endpoints.html
+++ b/docs/reference/endpoints.html
@@ -156,7 +156,17 @@ with the advantage that the SFORB model can also be used for metabolites.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'> <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'>endpoints</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in endpoints(fit): konnte Funktion "endpoints" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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'>endpoints</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
+#&gt; logical(0)
+#&gt;
+#&gt; $SFORB
+#&gt; logical(0)
+#&gt;
+#&gt; $distimes
+#&gt; DT50 DT90 DT50back
+#&gt; parent 1.785233 15.1479 4.559973
+#&gt; </div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/geometric_mean.html b/docs/reference/geometric_mean.html
index db737b2c..ca3d8808 100644
--- a/docs/reference/geometric_mean.html
+++ b/docs/reference/geometric_mean.html
@@ -150,7 +150,7 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'> <span class='fu'>geometric_mean</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>, <span class='fl'>9</span>))</div><div class='output co'>#&gt; <span class='error'>Error in geometric_mean(c(1, 3, 9)): konnte Funktion "geometric_mean" nicht finden</span></div><div class='input'> <span class='fu'>geometric_mean</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>, <span class='fl'>NA</span>))</div><div class='output co'>#&gt; <span class='error'>Error in geometric_mean(c(1, 3, NA)): konnte Funktion "geometric_mean" nicht finden</span></div><div class='input'> <span class='fu'>geometric_mean</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>, <span class='fl'>NA</span>), <span class='kw'>na.rm</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in geometric_mean(c(1, 3, NA), na.rm = TRUE): konnte Funktion "geometric_mean" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='fu'>geometric_mean</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>, <span class='fl'>9</span>))</div><div class='output co'>#&gt; [1] 3</div><div class='input'> <span class='fu'>geometric_mean</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>, <span class='fl'>NA</span>))</div><div class='output co'>#&gt; [1] NA</div><div class='input'> <span class='fu'>geometric_mean</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>, <span class='fl'>NA</span>), <span class='kw'>na.rm</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; [1] 1.732051</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html
index b75a9a91..760dd3aa 100644
--- a/docs/reference/ilr.html
+++ b/docs/reference/ilr.html
@@ -158,14 +158,15 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'># Order matters</span>
-<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>1</span>, <span class='fl'>10</span>))</div><div class='output co'>#&gt; <span class='error'>Error in ilr(c(0.1, 1, 10)): konnte Funktion "ilr" nicht finden</span></div><div class='input'><span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>1</span>, <span class='fl'>0.1</span>))</div><div class='output co'>#&gt; <span class='error'>Error in ilr(c(10, 1, 0.1)): konnte Funktion "ilr" nicht finden</span></div><div class='input'><span class='co'># Equal entries give ilr transformations with zeros as elements</span>
-<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))</div><div class='output co'>#&gt; <span class='error'>Error in ilr(c(3, 3, 3)): konnte Funktion "ilr" nicht finden</span></div><div class='input'><span class='co'># Almost equal entries give small numbers</span>
-<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.3</span>, <span class='fl'>0.4</span>, <span class='fl'>0.3</span>))</div><div class='output co'>#&gt; <span class='error'>Error in ilr(c(0.3, 0.4, 0.3)): konnte Funktion "ilr" nicht finden</span></div><div class='input'><span class='co'># Only the ratio between the numbers counts, not their sum</span>
-<span class='fu'>invilr</span>(<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.7</span>, <span class='fl'>0.29</span>, <span class='fl'>0.01</span>)))</div><div class='output co'>#&gt; <span class='error'>Error in invilr(ilr(c(0.7, 0.29, 0.01))): konnte Funktion "invilr" nicht finden</span></div><div class='input'><span class='fu'>invilr</span>(<span class='fu'>ilr</span>(<span class='fl'>2.1</span> * <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.7</span>, <span class='fl'>0.29</span>, <span class='fl'>0.01</span>)))</div><div class='output co'>#&gt; <span class='error'>Error in invilr(ilr(2.1 * c(0.7, 0.29, 0.01))): konnte Funktion "invilr" nicht finden</span></div><div class='input'><span class='co'># Inverse transformation of larger numbers gives unequal elements</span>
-<span class='fu'>invilr</span>(-<span class='fl'>10</span>)</div><div class='output co'>#&gt; <span class='error'>Error in invilr(-10): konnte Funktion "invilr" nicht finden</span></div><div class='input'><span class='fu'>invilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(-<span class='fl'>10</span>, <span class='fl'>0</span>))</div><div class='output co'>#&gt; <span class='error'>Error in invilr(c(-10, 0)): konnte Funktion "invilr" nicht finden</span></div><div class='input'><span class='co'># The sum of the elements of the inverse ilr is 1</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/sum'>sum</a></span>(<span class='fu'>invilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(-<span class='fl'>10</span>, <span class='fl'>0</span>)))</div><div class='output co'>#&gt; <span class='error'>Error in invilr(c(-10, 0)): konnte Funktion "invilr" nicht finden</span></div><div class='input'><span class='co'># This is why we do not need all elements of the inverse transformation to go back:</span>
+<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>1</span>, <span class='fl'>10</span>))</div><div class='output co'>#&gt; [1] -1.628174 -2.820079</div><div class='input'><span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>1</span>, <span class='fl'>0.1</span>))</div><div class='output co'>#&gt; [1] 1.628174 2.820079</div><div class='input'><span class='co'># Equal entries give ilr transformations with zeros as elements</span>
+<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))</div><div class='output co'>#&gt; [1] 0 0</div><div class='input'><span class='co'># Almost equal entries give small numbers</span>
+<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.3</span>, <span class='fl'>0.4</span>, <span class='fl'>0.3</span>))</div><div class='output co'>#&gt; [1] -0.2034219 0.1174457</div><div class='input'><span class='co'># Only the ratio between the numbers counts, not their sum</span>
+<span class='fu'>invilr</span>(<span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.7</span>, <span class='fl'>0.29</span>, <span class='fl'>0.01</span>)))</div><div class='output co'>#&gt; [1] 0.70 0.29 0.01</div><div class='input'><span class='fu'>invilr</span>(<span class='fu'>ilr</span>(<span class='fl'>2.1</span> * <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.7</span>, <span class='fl'>0.29</span>, <span class='fl'>0.01</span>)))</div><div class='output co'>#&gt; [1] 0.70 0.29 0.01</div><div class='input'><span class='co'># Inverse transformation of larger numbers gives unequal elements</span>
+<span class='fu'>invilr</span>(-<span class='fl'>10</span>)</div><div class='output co'>#&gt; [1] 7.213536e-07 9.999993e-01</div><div class='input'><span class='fu'>invilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(-<span class='fl'>10</span>, <span class='fl'>0</span>))</div><div class='output co'>#&gt; [1] 7.207415e-07 9.991507e-01 8.486044e-04</div><div class='input'><span class='co'># The sum of the elements of the inverse ilr is 1</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/sum'>sum</a></span>(<span class='fu'>invilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(-<span class='fl'>10</span>, <span class='fl'>0</span>)))</div><div class='output co'>#&gt; [1] 1</div><div class='input'><span class='co'># This is why we do not need all elements of the inverse transformation to go back:</span>
<span class='no'>a</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>)
-<span class='no'>b</span> <span class='kw'>&lt;-</span> <span class='fu'>invilr</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; <span class='error'>Error in invilr(a): konnte Funktion "invilr" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/length'>length</a></span>(<span class='no'>b</span>) <span class='co'># Four elements</span></div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'b' nicht gefunden</span></div><div class='input'><span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='no'>b</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='fl'>1</span> - <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/sum'>sum</a></span>(<span class='no'>b</span>[<span class='fl'>1</span>:<span class='fl'>3</span>]))) <span class='co'># Gives c(0.1, 0.3, 0.5)</span></div><div class='output co'>#&gt; <span class='error'>Error in ilr(c(b[1:3], 1 - sum(b[1:3]))): konnte Funktion "ilr" nicht finden</span></div></pre>
+<span class='no'>b</span> <span class='kw'>&lt;-</span> <span class='fu'>invilr</span>(<span class='no'>a</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/length'>length</a></span>(<span class='no'>b</span>) <span class='co'># Four elements</span></div><div class='output co'>#&gt; [1] 4</div><div class='input'><span class='fu'>ilr</span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='no'>b</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='fl'>1</span> - <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/sum'>sum</a></span>(<span class='no'>b</span>[<span class='fl'>1</span>:<span class='fl'>3</span>]))) <span class='co'># Gives c(0.1, 0.3, 0.5)</span></div><div class='output co'>#&gt; [1] 0.1 0.3 0.5</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html
index 303a337d..a23ea52b 100644
--- a/docs/reference/logLik.mkinfit.html
+++ b/docs/reference/logLik.mkinfit.html
@@ -195,10 +195,18 @@ In the case of iterative reweighting, the variances obtained by this
<pre class="examples"><div class='input'> <span class='no'>sfo_sfo</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='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='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)
- )</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", to = "m1"), m1 = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'> <span class='no'>d_t</span> <span class='kw'>&lt;-</span> <span class='no'>FOCUS_2006_D</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'>d_t</span> <span class='kw'>&lt;-</span> <span class='no'>FOCUS_2006_D</span>
<span class='no'>d_t</span>[<span class='fl'>23</span>:<span class='fl'>24</span>, <span class='st'>"value"</span>] <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>NA</span>, <span class='fl'>NA</span>) <span class='co'># can't cope with zero values at the moment</span>
- <span class='no'>f_nw</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'>d_t</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># no weighting (weights are unity)</span></div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(sfo_sfo, d_t, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='no'>f_obs</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'>d_t</span>, <span class='kw'>reweight.method</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='error'>Error in mkinfit(sfo_sfo, d_t, reweight.method = "obs", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='no'>f_tc</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'>d_t</span>, <span class='kw'>reweight.method</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='error'>Error in mkinfit(sfo_sfo, d_t, reweight.method = "tc", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='no'>d_t</span>$<span class='no'>err</span> <span class='kw'>&lt;-</span> <span class='no'>d_t</span>$<span class='no'>value</span> <span class='co'># Manual weighting assuming sigma ~ y</span>
- <span class='no'>f_man</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'>d_t</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(sfo_sfo, d_t, err = "err", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f_nw</span>, <span class='no'>f_obs</span>, <span class='no'>f_tc</span>, <span class='no'>f_man</span>)</div><div class='output co'>#&gt; <span class='error'>Error in AIC(f_nw, f_obs, f_tc, f_man): Objekt 'f_nw' nicht gefunden</span></div></pre>
+ <span class='no'>f_nw</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'>d_t</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># no weighting (weights are unity)</span>
+ <span class='no'>f_obs</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'>d_t</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='no'>f_tc</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'>d_t</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='no'>d_t</span>$<span class='no'>err</span> <span class='kw'>&lt;-</span> <span class='no'>d_t</span>$<span class='no'>value</span> <span class='co'># Manual weighting assuming sigma ~ y</span>
+ <span class='no'>f_man</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'>d_t</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f_nw</span>, <span class='no'>f_obs</span>, <span class='no'>f_tc</span>, <span class='no'>f_man</span>)</div><div class='output co'>#&gt; df AIC
+#&gt; f_nw 5 204.4619
+#&gt; f_obs 6 205.8727
+#&gt; f_tc 6 143.8773
+#&gt; f_man 4 291.8000</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html
index ee99d0c8..320bdd46 100644
--- a/docs/reference/max_twa_parent.html
+++ b/docs/reference/max_twa_parent.html
@@ -166,7 +166,9 @@ guidance.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'> <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'>max_twa_parent</span>(<span class='no'>fit</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>21</span>))</div><div class='output co'>#&gt; <span class='error'>Error in max_twa_parent(fit, c(7, 21)): konnte Funktion "max_twa_parent" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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'>max_twa_parent</span>(<span class='no'>fit</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>21</span>))</div><div class='output co'>#&gt; 7 21
+#&gt; 34.71343 18.22124 </div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html
index 86ba7566..93d7f197 100644
--- a/docs/reference/mccall81_245T.html
+++ b/docs/reference/mccall81_245T.html
@@ -157,11 +157,190 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <span class='no'>SFO_SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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'>"phenol"</span>),
<span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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'>"anisole"</span>),
- <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(T245 = list(type = "SFO", to = "phenol"), phenol = list(type = "SFO", to = "anisole"), anisole = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'> </div><div class='input'> <span class='no'>fit.1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit.1, data = FALSE): Objekt 'fit.1' nicht gefunden</span></div><div class='input'> <span class='co'># No convergence, no covariance matrix ...</span>
+ <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</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'> </div><div class='input'> <span class='no'>fit.1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:05 2019
+#&gt; Date of summary: Thu Jan 31 16:51:05 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245
+#&gt; d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol -
+#&gt; k_phenol_anisole * phenol
+#&gt; d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole
+#&gt;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 574 model solutions performed in 3.87 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; T245_0 100.9000 state
+#&gt; k_T245_sink 0.1000 deparm
+#&gt; k_T245_phenol 0.1001 deparm
+#&gt; k_phenol_sink 0.1002 deparm
+#&gt; k_phenol_anisole 0.1003 deparm
+#&gt; k_anisole_sink 0.1004 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; T245_0 100.900000 -Inf Inf
+#&gt; log_k_T245_sink -2.302585 -Inf Inf
+#&gt; log_k_T245_phenol -2.301586 -Inf Inf
+#&gt; log_k_phenol_sink -2.300587 -Inf Inf
+#&gt; log_k_phenol_anisole -2.299590 -Inf Inf
+#&gt; log_k_anisole_sink -2.298593 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; phenol_0 0 state
+#&gt; anisole_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; T245_0 103.9000 NA NA NA
+#&gt; log_k_T245_sink -4.1130 NA NA NA
+#&gt; log_k_T245_phenol -3.6120 NA NA NA
+#&gt; log_k_phenol_sink -25.0800 NA NA NA
+#&gt; log_k_phenol_anisole -0.9037 NA NA NA
+#&gt; log_k_anisole_sink -5.0090 NA NA NA
+#&gt;
+#&gt; Parameter correlation:</div><div class='output co'>#&gt; <span class='warning'>Warning: Could not estimate covariance matrix; singular system:</span></div><div class='output co'>#&gt; Could not estimate covariance matrix; singular system:
+#&gt;
+#&gt; Residual standard error: 2.78 on 18 degrees of freedom
+#&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; T245_0 1.039e+02 4.282e+01 7.236e-20 NA NA
+#&gt; k_T245_sink 1.636e-02 8.901e-01 1.926e-01 NA NA
+#&gt; k_T245_phenol 2.701e-02 1.504e+00 7.498e-02 NA NA
+#&gt; k_phenol_sink 1.286e-11 4.575e-11 5.000e-01 NA NA
+#&gt; k_phenol_anisole 4.051e-01 2.518e+00 1.075e-02 NA NA
+#&gt; k_anisole_sink 6.679e-03 8.146e+00 9.469e-08 NA NA
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 10.070 6 16
+#&gt; T245 7.908 3 5
+#&gt; phenol 106.445 2 5
+#&gt; anisole 5.379 1 6
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; T245_sink 3.772e-01
+#&gt; T245_phenol 6.228e-01
+#&gt; phenol_sink 3.175e-11
+#&gt; phenol_anisole 1.000e+00
+#&gt; anisole_sink 1.000e+00
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; T245 15.982 53.091
+#&gt; phenol 1.711 5.685
+#&gt; anisole 103.784 344.763</div><div class='input'> <span class='co'># No convergence, no covariance matrix ...</span>
<span class='co'># k_phenol_sink is really small, therefore fix it to zero</span>
<span class='no'>fit.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>),
<span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_phenol_sink</span> <span class='kw'>=</span> <span class='fl'>0</span>),
- <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='st'>"k_phenol_sink"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"), parms.ini = c(k_phenol_sink = 0), fixed_parms = "k_phenol_sink", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit.2, data = FALSE): Objekt 'fit.2' nicht gefunden</span></div><div class='input'> </div></pre>
+ <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='st'>"k_phenol_sink"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:07 2019
+#&gt; Date of summary: Thu Jan 31 16:51:07 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245
+#&gt; d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol -
+#&gt; k_phenol_anisole * phenol
+#&gt; d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole
+#&gt;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 246 model solutions performed in 1.618 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; T245_0 100.9000 state
+#&gt; k_T245_sink 0.1000 deparm
+#&gt; k_T245_phenol 0.1001 deparm
+#&gt; k_phenol_anisole 0.1002 deparm
+#&gt; k_anisole_sink 0.1003 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; T245_0 100.900000 -Inf Inf
+#&gt; log_k_T245_sink -2.302585 -Inf Inf
+#&gt; log_k_T245_phenol -2.301586 -Inf Inf
+#&gt; log_k_phenol_anisole -2.300587 -Inf Inf
+#&gt; log_k_anisole_sink -2.299590 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; phenol_0 0 state
+#&gt; anisole_0 0 state
+#&gt; k_phenol_sink 0 deparm
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; T245_0 103.9000 2.35200 98.930 108.8000
+#&gt; log_k_T245_sink -4.1130 0.13250 -4.390 -3.8350
+#&gt; log_k_T245_phenol -3.6120 0.05002 -3.716 -3.5070
+#&gt; log_k_phenol_anisole -0.9037 0.30580 -1.544 -0.2637
+#&gt; log_k_anisole_sink -5.0090 0.11180 -5.243 -4.7750
+#&gt;
+#&gt; Parameter correlation:
+#&gt; T245_0 log_k_T245_sink log_k_T245_phenol
+#&gt; T245_0 1.00000 0.63761 -0.1742
+#&gt; log_k_T245_sink 0.63761 1.00000 -0.3831
+#&gt; log_k_T245_phenol -0.17416 -0.38313 1.0000
+#&gt; log_k_phenol_anisole -0.05948 0.08745 -0.3047
+#&gt; log_k_anisole_sink -0.16208 -0.60469 0.5227
+#&gt; log_k_phenol_anisole log_k_anisole_sink
+#&gt; T245_0 -0.05948 -0.1621
+#&gt; log_k_T245_sink 0.08745 -0.6047
+#&gt; log_k_T245_phenol -0.30470 0.5227
+#&gt; log_k_phenol_anisole 1.00000 -0.1774
+#&gt; log_k_anisole_sink -0.17744 1.0000
+#&gt;
+#&gt; Residual standard error: 2.706 on 19 degrees of freedom
+#&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; T245_0 1.039e+02 44.160 6.462e-21 98.930000 108.80000
+#&gt; k_T245_sink 1.636e-02 7.545 1.978e-07 0.012400 0.02159
+#&gt; k_T245_phenol 2.701e-02 19.990 1.607e-14 0.024320 0.02999
+#&gt; k_phenol_anisole 4.051e-01 3.270 2.014e-03 0.213600 0.76820
+#&gt; k_anisole_sink 6.679e-03 8.942 1.544e-08 0.005285 0.00844
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 9.831 5 17
+#&gt; T245 7.908 3 5
+#&gt; phenol 99.808 1 6
+#&gt; anisole 5.379 1 6
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; T245_sink 0.3772
+#&gt; T245_phenol 0.6228
+#&gt; phenol_anisole 1.0000
+#&gt; phenol_sink 0.0000
+#&gt; anisole_sink 1.0000
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; T245 15.982 53.091
+#&gt; phenol 1.711 5.685
+#&gt; anisole 103.784 344.763</div><div class='input'> </div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html
index 9a7e3c61..290d3e76 100644
--- a/docs/reference/mkin_long_to_wide.html
+++ b/docs/reference/mkin_long_to_wide.html
@@ -160,7 +160,29 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'><span class='fu'>mkin_long_to_wide</span>(<span class='no'>FOCUS_2006_D</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkin_long_to_wide(FOCUS_2006_D): konnte Funktion "mkin_long_to_wide" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'><span class='fu'>mkin_long_to_wide</span>(<span class='no'>FOCUS_2006_D</span>)</div><div class='output co'>#&gt; time parent m1
+#&gt; 1 0 99.46 0.00
+#&gt; 2 0 102.04 0.00
+#&gt; 3 1 93.50 4.84
+#&gt; 4 1 92.50 5.64
+#&gt; 5 3 63.23 12.91
+#&gt; 6 3 68.99 12.96
+#&gt; 7 7 52.32 22.97
+#&gt; 8 7 55.13 24.47
+#&gt; 9 14 27.27 41.69
+#&gt; 10 14 26.64 33.21
+#&gt; 11 21 11.50 44.37
+#&gt; 12 21 11.64 46.44
+#&gt; 13 35 2.85 41.22
+#&gt; 14 35 2.91 37.95
+#&gt; 15 50 0.69 41.19
+#&gt; 16 50 0.63 40.01
+#&gt; 17 75 0.05 40.09
+#&gt; 18 75 0.06 33.85
+#&gt; 19 100 NA 31.04
+#&gt; 20 100 NA 33.13
+#&gt; 21 120 NA 25.15
+#&gt; 22 120 NA 33.31</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html
index 5e7df90d..d7438fcf 100644
--- a/docs/reference/mkin_wide_to_long.html
+++ b/docs/reference/mkin_wide_to_long.html
@@ -154,7 +154,13 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>wide</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/data.frame'>data.frame</a></span>(<span class='kw'>t</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>), <span class='kw'>x</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>7</span>), <span class='kw'>y</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>4</span>,<span class='fl'>5</span>))
-<span class='fu'>mkin_wide_to_long</span>(<span class='no'>wide</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkin_wide_to_long(wide): konnte Funktion "mkin_wide_to_long" nicht finden</span></div></pre>
+<span class='fu'>mkin_wide_to_long</span>(<span class='no'>wide</span>)</div><div class='output co'>#&gt; name time value
+#&gt; 1 x 1 1
+#&gt; 2 x 2 4
+#&gt; 3 x 3 7
+#&gt; 4 y 1 3
+#&gt; 5 y 2 4
+#&gt; 6 y 3 5</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html
index 1686679b..35086a61 100644
--- a/docs/reference/mkinerrmin.html
+++ b/docs/reference/mkinerrmin.html
@@ -175,8 +175,16 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>SFO_SFO</span> <span class='kw'>=</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='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='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='error'>Error in mkinmod(parent = mkinsub("SFO", to = "m1"), m1 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
-<span class='no'>fit_FOCUS_D</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</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='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Round'>round</a></span>(<span class='fu'>mkinerrmin</span>(<span class='no'>fit_FOCUS_D</span>), <span class='fl'>4</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinerrmin(fit_FOCUS_D): konnte Funktion "mkinerrmin" nicht finden</span></div><div class='input'> <span class='no'>fit_FOCUS_E</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_E</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Round'>round</a></span>(<span class='fu'>mkinerrmin</span>(<span class='no'>fit_FOCUS_E</span>), <span class='fl'>4</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinerrmin(fit_FOCUS_E): konnte Funktion "mkinerrmin" nicht finden</span></div></pre>
+ <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'>fit_FOCUS_D</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Round'>round</a></span>(<span class='fu'>mkinerrmin</span>(<span class='no'>fit_FOCUS_D</span>), <span class='fl'>4</span>)</div><div class='output co'>#&gt; err.min n.optim df
+#&gt; All data 0.0640 4 15
+#&gt; parent 0.0646 2 7
+#&gt; m1 0.0469 2 8</div><div class='input'> <span class='no'>fit_FOCUS_E</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_E</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Round'>round</a></span>(<span class='fu'>mkinerrmin</span>(<span class='no'>fit_FOCUS_E</span>), <span class='fl'>4</span>)</div><div class='output co'>#&gt; err.min n.optim df
+#&gt; All data 0.1544 4 13
+#&gt; parent 0.1659 2 7
+#&gt; m1 0.1095 2 6</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index bd691770..42845d68 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -435,40 +435,914 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'># Use shorthand notation for parent only degradation</span>
-<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>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit): Objekt 'fit' nicht gefunden</span></div><div class='input'>
+<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://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:10 2019
+#&gt; Date of summary: Thu Jan 31 16:51:10 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 with method Port using 64 model solutions performed in 0.162 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 85.1 state
+#&gt; alpha 1.0 deparm
+#&gt; beta 10.0 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 85.100000 -Inf Inf
+#&gt; log_alpha 0.000000 -Inf Inf
+#&gt; log_beta 2.302585 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; None
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 85.87000 2.2460 80.38000 91.3700
+#&gt; log_alpha 0.05192 0.1605 -0.34080 0.4446
+#&gt; log_beta 0.65100 0.2801 -0.03452 1.3360
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_alpha log_beta
+#&gt; parent_0 1.0000 -0.2033 -0.3624
+#&gt; log_alpha -0.2033 1.0000 0.9547
+#&gt; log_beta -0.3624 0.9547 1.0000
+#&gt;
+#&gt; Residual standard error: 2.275 on 6 degrees of freedom
+#&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 85.870 38.230 1.069e-08 80.3800 91.370
+#&gt; alpha 1.053 6.231 3.953e-04 0.7112 1.560
+#&gt; beta 1.917 3.570 5.895e-03 0.9661 3.806
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.657 3 6
+#&gt; parent 6.657 3 6
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90 DT50back
+#&gt; parent 1.785 15.15 4.56
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual
+#&gt; 0 parent 85.1 85.875 -0.7749
+#&gt; 1 parent 57.9 55.191 2.7091
+#&gt; 3 parent 29.9 31.845 -1.9452
+#&gt; 7 parent 14.6 17.012 -2.4124
+#&gt; 14 parent 9.7 9.241 0.4590
+#&gt; 28 parent 6.6 4.754 1.8460
+#&gt; 63 parent 4.0 2.102 1.8977
+#&gt; 91 parent 3.9 1.441 2.4590
+#&gt; 119 parent 0.6 1.092 -0.4919</div><div class='input'>
<span class='co'># One parent compound, one metabolite, both single first order.</span>
<span class='co'># Use mkinsub for convenience in model formulation. Pathway to sink included per default.</span>
<span class='no'>SFO_SFO</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>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</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>))</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://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>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='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in coef(fit): Objekt 'fit' nicht gefunden</span></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; <span class='error'>Error in endpoints(fit): konnte Funktion "endpoints" nicht finden</span></div><div class='input'><span class='co'># deSolve is slower when no C compiler (gcc) was available during model generation</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; User System verstrichen
+#&gt; 1.022 0.000 1.025 </div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
+#&gt; 99.59848 -3.03822 -2.98030 -5.24750 </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;
+#&gt; $SFORB
+#&gt; logical(0)
+#&gt;
+#&gt; $distimes
+#&gt; DT50 DT90
+#&gt; parent 7.022929 23.32967
+#&gt; m1 131.760712 437.69961
+#&gt; </div><div class='input'><span class='co'># deSolve is slower when no C compiler (gcc) was available during model generation</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(<span class='no'>fit.deSolve</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'>"deSolve"</span>)))</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve"): konnte Funktion "mkinfit" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; <span class='error'>Error in coef(fit.deSolve): Objekt 'fit.deSolve' nicht gefunden</span></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; <span class='error'>Error in endpoints(fit.deSolve): konnte Funktion "endpoints" nicht finden</span></div><div class='input'>
+ <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))</div><div class='output co'>#&gt; Model cost at call 1 : 18915.53
+#&gt; Model cost at call 2 : 18915.53
+#&gt; Model cost at call 6 : 11424.02
+#&gt; Model cost at call 10 : 11424
+#&gt; Model cost at call 12 : 4094.396
+#&gt; Model cost at call 16 : 4094.396
+#&gt; Model cost at call 19 : 1340.595
+#&gt; Model cost at call 20 : 1340.593
+#&gt; Model cost at call 25 : 1072.239
+#&gt; Model cost at call 28 : 1072.236
+#&gt; Model cost at call 30 : 874.2615
+#&gt; Model cost at call 33 : 874.2611
+#&gt; Model cost at call 35 : 616.2377
+#&gt; Model cost at call 37 : 616.2372
+#&gt; Model cost at call 40 : 467.4386
+#&gt; Model cost at call 42 : 467.4381
+#&gt; Model cost at call 46 : 398.2914
+#&gt; Model cost at call 48 : 398.2914
+#&gt; Model cost at call 49 : 398.2913
+#&gt; Model cost at call 51 : 395.0712
+#&gt; Model cost at call 54 : 395.0711
+#&gt; Model cost at call 56 : 378.3298
+#&gt; Model cost at call 59 : 378.3298
+#&gt; Model cost at call 62 : 376.9812
+#&gt; Model cost at call 64 : 376.9811
+#&gt; Model cost at call 67 : 375.2085
+#&gt; Model cost at call 69 : 375.2085
+#&gt; Model cost at call 70 : 375.2085
+#&gt; Model cost at call 71 : 375.2085
+#&gt; Model cost at call 72 : 374.5723
+#&gt; Model cost at call 74 : 374.5723
+#&gt; Model cost at call 77 : 374.0075
+#&gt; Model cost at call 79 : 374.0075
+#&gt; Model cost at call 80 : 374.0075
+#&gt; Model cost at call 82 : 373.1711
+#&gt; Model cost at call 84 : 373.1711
+#&gt; Model cost at call 87 : 372.6445
+#&gt; Model cost at call 88 : 372.1614
+#&gt; Model cost at call 90 : 372.1614
+#&gt; Model cost at call 91 : 372.1614
+#&gt; Model cost at call 94 : 371.6464
+#&gt; Model cost at call 99 : 371.4299
+#&gt; Model cost at call 101 : 371.4299
+#&gt; Model cost at call 104 : 371.4071
+#&gt; Model cost at call 106 : 371.4071
+#&gt; Model cost at call 107 : 371.4071
+#&gt; Model cost at call 109 : 371.2524
+#&gt; Model cost at call 113 : 371.2524
+#&gt; Model cost at call 114 : 371.2136
+#&gt; Model cost at call 115 : 371.2136
+#&gt; Model cost at call 116 : 371.2136
+#&gt; Model cost at call 119 : 371.2134
+#&gt; Model cost at call 120 : 371.2134
+#&gt; Model cost at call 122 : 371.2134
+#&gt; Model cost at call 123 : 371.2134
+#&gt; Model cost at call 125 : 371.2134
+#&gt; Model cost at call 126 : 371.2134
+#&gt; Model cost at call 135 : 371.2134
+#&gt; Model cost at call 146 : 371.2134
+#&gt; Optimisation by method Port successfully terminated.
+#&gt; User System verstrichen
+#&gt; 0.823 0.000 0.823 </div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
+#&gt; 99.59848 -3.03822 -2.98030 -5.24750 </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;
+#&gt; $SFORB
+#&gt; logical(0)
+#&gt;
+#&gt; $distimes
+#&gt; DT50 DT90
+#&gt; parent 7.022929 23.32967
+#&gt; m1 131.760711 437.69961
+#&gt; </div><div class='input'>
# Use stepwise fitting, using optimised parameters from parent only fit, FOMC
</div><div class='input'><span class='no'>FOMC_SFO</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'>"FOMC"</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>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("FOMC", "m1"), m1 = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
-<span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</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='error'>Error in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='co'># Use starting parameters from parent only FOMC fit</span>
-<span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</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='error'>Error in mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
- <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.FOMC</span>$<span class='no'>bparms.ode</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE, parms.ini = fit.FOMC$bparms.ode): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
+<span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='co'># Use starting parameters from parent only FOMC fit</span>
+<span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
+ <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.FOMC</span>$<span class='no'>bparms.ode</span>)
+
<span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, SFORB</span>
<span class='no'>SFORB_SFO</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://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFORB"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
- <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = list(type = "SFORB", to = "m1", sink = TRUE), m1 = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
-<span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</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='error'>Error in mkinfit(SFORB_SFO, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='no'>fit.SFORB_SFO.deSolve</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>,
- <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='co'># Use starting parameters from parent only SFORB fit (not really needed in this case)</span>
-<span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</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='error'>Error in mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</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='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>fit.SFORB_SFO.deSolve</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>,
+ <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='co'># Use starting parameters from parent only SFORB fit (not really needed in this case)</span>
+<span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='input'>
</div><div class='input'><span class='co'># Weighted fits, including IRLS</span>
<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='error'>Error in mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</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='error'>Error in mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(f.noweight): Objekt 'f.noweight' nicht gefunden</span></div><div class='input'><span class='no'>f.irls</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'>reweight.method</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='error'>Error in mkinfit(SFO_SFO.ff, FOCUS_2006_D, reweight.method = "obs", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.irls</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(f.irls): Objekt 'f.irls' nicht gefunden</span></div><div class='input'><span class='no'>f.w.mean</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'>weight</span> <span class='kw'>=</span> <span class='st'>"mean"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.mean</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(f.w.mean): Objekt 'f.w.mean' nicht gefunden</span></div><div class='input'><span class='no'>f.w.value</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"value"</span>,
- <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), err = "value", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.value</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(f.w.value): Objekt 'f.w.value' nicht gefunden</span></div><div class='input'>
+ <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>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:22 2019
+#&gt; Date of summary: Thu Jan 31 16:51:22 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 186 model solutions performed in 0.872 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.60000 1.61400 96.3300 102.9000
+#&gt; log_k_parent -2.31600 0.04187 -2.4010 -2.2310
+#&gt; log_k_m1 -5.24800 0.13610 -5.5230 -4.9720
+#&gt; f_parent_ilr_1 0.04096 0.06477 -0.0904 0.1723
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.0000 0.5178 -0.1701 -0.5489
+#&gt; log_k_parent 0.5178 1.0000 -0.3285 -0.5451
+#&gt; log_k_m1 -0.1701 -0.3285 1.0000 0.7466
+#&gt; f_parent_ilr_1 -0.5489 -0.5451 0.7466 1.0000
+#&gt;
+#&gt; Residual standard error: 3.211 on 36 degrees of freedom
+#&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 99.600000 61.720 2.024e-38 96.330000 1.029e+02
+#&gt; k_parent 0.098700 23.880 5.700e-24 0.090660 1.074e-01
+#&gt; k_m1 0.005261 7.349 5.758e-09 0.003992 6.933e-03
+#&gt; f_parent_to_m1 0.514500 22.490 4.375e-23 0.468100 5.606e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.398 4 15
+#&gt; parent 6.459 2 7
+#&gt; m1 4.690 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5145
+#&gt; parent_sink 0.4855
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 7.023 23.33
+#&gt; m1 131.761 437.70
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual
+#&gt; 0 parent 99.46 99.59848 -1.385e-01
+#&gt; 0 parent 102.04 99.59848 2.442e+00
+#&gt; 1 parent 93.50 90.23787 3.262e+00
+#&gt; 1 parent 92.50 90.23787 2.262e+00
+#&gt; 3 parent 63.23 74.07319 -1.084e+01
+#&gt; 3 parent 68.99 74.07319 -5.083e+00
+#&gt; 7 parent 52.32 49.91206 2.408e+00
+#&gt; 7 parent 55.13 49.91206 5.218e+00
+#&gt; 14 parent 27.27 25.01257 2.257e+00
+#&gt; 14 parent 26.64 25.01257 1.627e+00
+#&gt; 21 parent 11.50 12.53462 -1.035e+00
+#&gt; 21 parent 11.64 12.53462 -8.946e-01
+#&gt; 35 parent 2.85 3.14787 -2.979e-01
+#&gt; 35 parent 2.91 3.14787 -2.379e-01
+#&gt; 50 parent 0.69 0.71624 -2.624e-02
+#&gt; 50 parent 0.63 0.71624 -8.624e-02
+#&gt; 75 parent 0.05 0.06074 -1.074e-02
+#&gt; 75 parent 0.06 0.06074 -7.381e-04
+#&gt; 0 m1 0.00 0.00000 0.000e+00
+#&gt; 0 m1 0.00 0.00000 0.000e+00
+#&gt; 1 m1 4.84 4.80296 3.704e-02
+#&gt; 1 m1 5.64 4.80296 8.370e-01
+#&gt; 3 m1 12.91 13.02400 -1.140e-01
+#&gt; 3 m1 12.96 13.02400 -6.400e-02
+#&gt; 7 m1 22.97 25.04476 -2.075e+00
+#&gt; 7 m1 24.47 25.04476 -5.748e-01
+#&gt; 14 m1 41.69 36.69002 5.000e+00
+#&gt; 14 m1 33.21 36.69002 -3.480e+00
+#&gt; 21 m1 44.37 41.65310 2.717e+00
+#&gt; 21 m1 46.44 41.65310 4.787e+00
+#&gt; 35 m1 41.22 43.31312 -2.093e+00
+#&gt; 35 m1 37.95 43.31312 -5.363e+00
+#&gt; 50 m1 41.19 41.21831 -2.831e-02
+#&gt; 50 m1 40.01 41.21831 -1.208e+00
+#&gt; 75 m1 40.09 36.44703 3.643e+00
+#&gt; 75 m1 33.85 36.44703 -2.597e+00
+#&gt; 100 m1 31.04 31.98163 -9.416e-01
+#&gt; 100 m1 33.13 31.98163 1.148e+00
+#&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.irls</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'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.irls</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:24 2019
+#&gt; Date of summary: Thu Jan 31 16:51:24 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 551 model solutions performed in 2.558 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Iterative reweighting with method obs
+#&gt; Final mean squared residuals of observed variables:
+#&gt; parent m1
+#&gt; 11.573407 7.407845
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.67000 1.79200 96.04000 103.300
+#&gt; log_k_parent -2.31200 0.04560 -2.40400 -2.219
+#&gt; log_k_m1 -5.25100 0.12510 -5.50500 -4.998
+#&gt; f_parent_ilr_1 0.03785 0.06318 -0.09027 0.166
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.0000 0.5083 -0.1979 -0.6148
+#&gt; log_k_parent 0.5083 1.0000 -0.3894 -0.6062
+#&gt; log_k_m1 -0.1979 -0.3894 1.0000 0.7417
+#&gt; f_parent_ilr_1 -0.6148 -0.6062 0.7417 1.0000
+#&gt;
+#&gt; Residual standard error: 1.054 on 36 degrees of freedom
+#&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 99.67000 55.630 8.185e-37 96.040000 1.033e+02
+#&gt; k_parent 0.09906 21.930 1.016e-22 0.090310 1.087e-01
+#&gt; k_m1 0.00524 7.996 8.486e-10 0.004066 6.753e-03
+#&gt; f_parent_to_m1 0.51340 23.000 2.038e-23 0.468100 5.584e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.399 4 15
+#&gt; parent 6.466 2 7
+#&gt; m1 4.679 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5134
+#&gt; parent_sink 0.4866
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 6.997 23.24
+#&gt; m1 132.282 439.43
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual err
+#&gt; 0 parent 99.46 99.67218 -2.122e-01 3.402
+#&gt; 0 parent 102.04 99.67218 2.368e+00 3.402
+#&gt; 1 parent 93.50 90.27153 3.228e+00 3.402
+#&gt; 1 parent 92.50 90.27153 2.228e+00 3.402
+#&gt; 3 parent 63.23 74.04648 -1.082e+01 3.402
+#&gt; 3 parent 68.99 74.04648 -5.056e+00 3.402
+#&gt; 7 parent 52.32 49.82092 2.499e+00 3.402
+#&gt; 7 parent 55.13 49.82092 5.309e+00 3.402
+#&gt; 14 parent 27.27 24.90288 2.367e+00 3.402
+#&gt; 14 parent 26.64 24.90288 1.737e+00 3.402
+#&gt; 21 parent 11.50 12.44765 -9.476e-01 3.402
+#&gt; 21 parent 11.64 12.44765 -8.076e-01 3.402
+#&gt; 35 parent 2.85 3.11002 -2.600e-01 3.402
+#&gt; 35 parent 2.91 3.11002 -2.000e-01 3.402
+#&gt; 50 parent 0.69 0.70374 -1.374e-02 3.402
+#&gt; 50 parent 0.63 0.70374 -7.374e-02 3.402
+#&gt; 75 parent 0.05 0.05913 -9.134e-03 3.402
+#&gt; 75 parent 0.06 0.05913 8.662e-04 3.402
+#&gt; 0 m1 0.00 0.00000 0.000e+00 2.722
+#&gt; 0 m1 0.00 0.00000 0.000e+00 2.722
+#&gt; 1 m1 4.84 4.81328 2.672e-02 2.722
+#&gt; 1 m1 5.64 4.81328 8.267e-01 2.722
+#&gt; 3 m1 12.91 13.04779 -1.378e-01 2.722
+#&gt; 3 m1 12.96 13.04779 -8.779e-02 2.722
+#&gt; 7 m1 22.97 25.07615 -2.106e+00 2.722
+#&gt; 7 m1 24.47 25.07615 -6.062e-01 2.722
+#&gt; 14 m1 41.69 36.70729 4.983e+00 2.722
+#&gt; 14 m1 33.21 36.70729 -3.497e+00 2.722
+#&gt; 21 m1 44.37 41.65050 2.720e+00 2.722
+#&gt; 21 m1 46.44 41.65050 4.790e+00 2.722
+#&gt; 35 m1 41.22 43.28866 -2.069e+00 2.722
+#&gt; 35 m1 37.95 43.28866 -5.339e+00 2.722
+#&gt; 50 m1 41.19 41.19339 -3.386e-03 2.722
+#&gt; 50 m1 40.01 41.19339 -1.183e+00 2.722
+#&gt; 75 m1 40.09 36.43820 3.652e+00 2.722
+#&gt; 75 m1 33.85 36.43820 -2.588e+00 2.722
+#&gt; 100 m1 31.04 31.98971 -9.497e-01 2.722
+#&gt; 100 m1 33.13 31.98971 1.140e+00 2.722
+#&gt; 120 m1 25.15 28.80898 -3.659e+00 2.722
+#&gt; 120 m1 33.31 28.80898 4.501e+00 2.722</div><div class='input'><span class='no'>f.w.mean</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'>weight</span> <span class='kw'>=</span> <span class='st'>"mean"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.mean</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:25 2019
+#&gt; Date of summary: Thu Jan 31 16:51:25 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 155 model solutions performed in 0.711 s
+#&gt;
+#&gt; Weighting: mean
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.7300 1.93200 95.81000 103.6000
+#&gt; log_k_parent -2.3090 0.04837 -2.40700 -2.2110
+#&gt; log_k_m1 -5.2550 0.12070 -5.49900 -5.0100
+#&gt; f_parent_ilr_1 0.0354 0.06344 -0.09327 0.1641
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.0000 0.5004 -0.2143 -0.6514
+#&gt; log_k_parent 0.5004 1.0000 -0.4282 -0.6383
+#&gt; log_k_m1 -0.2143 -0.4282 1.0000 0.7390
+#&gt; f_parent_ilr_1 -0.6514 -0.6383 0.7390 1.0000
+#&gt;
+#&gt; Residual standard error: 0.09829 on 36 degrees of freedom
+#&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 99.730000 51.630 1.166e-35 95.81000 1.036e+02
+#&gt; k_parent 0.099360 20.670 7.304e-22 0.09007 1.096e-01
+#&gt; k_m1 0.005224 8.287 3.649e-10 0.00409 6.672e-03
+#&gt; f_parent_to_m1 0.512500 22.860 2.497e-23 0.46710 5.578e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.401 4 15
+#&gt; parent 6.473 2 7
+#&gt; m1 4.671 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5125
+#&gt; parent_sink 0.4875
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 6.976 23.18
+#&gt; m1 132.696 440.81
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual
+#&gt; 0 parent 99.46 99.73057 -0.270570
+#&gt; 0 parent 102.04 99.73057 2.309430
+#&gt; 1 parent 93.50 90.29805 3.201945
+#&gt; 1 parent 92.50 90.29805 2.201945
+#&gt; 3 parent 63.23 74.02503 -10.795028
+#&gt; 3 parent 68.99 74.02503 -5.035028
+#&gt; 7 parent 52.32 49.74838 2.571618
+#&gt; 7 parent 55.13 49.74838 5.381618
+#&gt; 14 parent 27.27 24.81588 2.454124
+#&gt; 14 parent 26.64 24.81588 1.824124
+#&gt; 21 parent 11.50 12.37885 -0.878849
+#&gt; 21 parent 11.64 12.37885 -0.738849
+#&gt; 35 parent 2.85 3.08022 -0.230219
+#&gt; 35 parent 2.91 3.08022 -0.170219
+#&gt; 50 parent 0.69 0.69396 -0.003958
+#&gt; 50 parent 0.63 0.69396 -0.063958
+#&gt; 75 parent 0.05 0.05789 -0.007888
+#&gt; 75 parent 0.06 0.05789 0.002112
+#&gt; 0 m1 0.00 0.00000 0.000000
+#&gt; 0 m1 0.00 0.00000 0.000000
+#&gt; 1 m1 4.84 4.82149 0.018512
+#&gt; 1 m1 5.64 4.82149 0.818512
+#&gt; 3 m1 12.91 13.06669 -0.156692
+#&gt; 3 m1 12.96 13.06669 -0.106692
+#&gt; 7 m1 22.97 25.10106 -2.131058
+#&gt; 7 m1 24.47 25.10106 -0.631058
+#&gt; 14 m1 41.69 36.72092 4.969077
+#&gt; 14 m1 33.21 36.72092 -3.510923
+#&gt; 21 m1 44.37 41.64835 2.721647
+#&gt; 21 m1 46.44 41.64835 4.791647
+#&gt; 35 m1 41.22 43.26923 -2.049225
+#&gt; 35 m1 37.95 43.26923 -5.319225
+#&gt; 50 m1 41.19 41.17364 0.016361
+#&gt; 50 m1 40.01 41.17364 -1.163639
+#&gt; 75 m1 40.09 36.43122 3.658776
+#&gt; 75 m1 33.85 36.43122 -2.581224
+#&gt; 100 m1 31.04 31.99612 -0.956124
+#&gt; 100 m1 33.13 31.99612 1.133876
+#&gt; 120 m1 25.15 28.82413 -3.674128
+#&gt; 120 m1 33.31 28.82413 4.485872</div><div class='input'><span class='no'>f.w.value</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"value"</span>,
+ <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.value</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:26 2019
+#&gt; Date of summary: Thu Jan 31 16:51:26 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 174 model solutions performed in 0.807 s
+#&gt;
+#&gt; Weighting: manual
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.6600 2.712000 94.14000 105.2000
+#&gt; log_k_parent -2.2980 0.008118 -2.31500 -2.2820
+#&gt; log_k_m1 -5.2410 0.096690 -5.43800 -5.0450
+#&gt; f_parent_ilr_1 0.0231 0.057990 -0.09474 0.1409
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.00000 0.6843 -0.08687 -0.7564
+#&gt; log_k_parent 0.68435 1.0000 -0.12695 -0.5812
+#&gt; log_k_m1 -0.08687 -0.1269 1.00000 0.5195
+#&gt; f_parent_ilr_1 -0.75644 -0.5812 0.51952 1.0000
+#&gt;
+#&gt; Residual standard error: 0.08396 on 34 degrees of freedom
+#&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 99.660000 36.75 2.957e-29 94.14000 1.052e+02
+#&gt; k_parent 0.100400 123.20 5.927e-47 0.09878 1.021e-01
+#&gt; k_m1 0.005295 10.34 2.447e-12 0.00435 6.444e-03
+#&gt; f_parent_to_m1 0.508200 24.79 1.184e-23 0.46660 5.497e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.461 4 15
+#&gt; parent 6.520 2 7
+#&gt; m1 4.744 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5082
+#&gt; parent_sink 0.4918
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 6.902 22.93
+#&gt; m1 130.916 434.89
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual err
+#&gt; 0 parent 99.46 99.65571 -0.195715 99.46
+#&gt; 0 parent 102.04 99.65571 2.384285 102.04
+#&gt; 1 parent 93.50 90.13383 3.366170 93.50
+#&gt; 1 parent 92.50 90.13383 2.366170 92.50
+#&gt; 3 parent 63.23 73.73252 -10.502518 63.23
+#&gt; 3 parent 68.99 73.73252 -4.742518 68.99
+#&gt; 7 parent 52.32 49.34027 2.979728 52.32
+#&gt; 7 parent 55.13 49.34027 5.789728 55.13
+#&gt; 14 parent 27.27 24.42873 2.841271 27.27
+#&gt; 14 parent 26.64 24.42873 2.211271 26.64
+#&gt; 21 parent 11.50 12.09484 -0.594842 11.50
+#&gt; 21 parent 11.64 12.09484 -0.454842 11.64
+#&gt; 35 parent 2.85 2.96482 -0.114824 2.85
+#&gt; 35 parent 2.91 2.96482 -0.054824 2.91
+#&gt; 50 parent 0.69 0.65733 0.032670 0.69
+#&gt; 50 parent 0.63 0.65733 -0.027330 0.63
+#&gt; 75 parent 0.05 0.05339 -0.003386 0.05
+#&gt; 75 parent 0.06 0.05339 0.006614 0.06
+#&gt; 1 m1 4.84 4.82570 0.014301 4.84
+#&gt; 1 m1 5.64 4.82570 0.814301 5.64
+#&gt; 3 m1 12.91 13.06402 -0.154020 12.91
+#&gt; 3 m1 12.96 13.06402 -0.104020 12.96
+#&gt; 7 m1 22.97 25.04656 -2.076564 22.97
+#&gt; 7 m1 24.47 25.04656 -0.576564 24.47
+#&gt; 14 m1 41.69 36.53601 5.153988 41.69
+#&gt; 14 m1 33.21 36.53601 -3.326012 33.21
+#&gt; 21 m1 44.37 41.34639 3.023609 44.37
+#&gt; 21 m1 46.44 41.34639 5.093609 46.44
+#&gt; 35 m1 41.22 42.82669 -1.606690 41.22
+#&gt; 35 m1 37.95 42.82669 -4.876690 37.95
+#&gt; 50 m1 41.19 40.67342 0.516578 41.19
+#&gt; 50 m1 40.01 40.67342 -0.663422 40.01
+#&gt; 75 m1 40.09 35.91105 4.178947 40.09
+#&gt; 75 m1 33.85 35.91105 -2.061053 33.85
+#&gt; 100 m1 31.04 31.48161 -0.441612 31.04
+#&gt; 100 m1 33.13 31.48161 1.648388 33.13
+#&gt; 120 m1 25.15 28.32018 -3.170181 25.15
+#&gt; 120 m1 33.31 28.32018 4.989819 33.31</div><div class='input'>
</div><div class='input'><span class='co'># Manual weighting</span>
<span class='no'>dw</span> <span class='kw'>&lt;-</span> <span class='no'>FOCUS_2006_D</span>
<span class='no'>errors</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>1</span>)
<span class='no'>dw</span>$<span class='no'>err.man</span> <span class='kw'>&lt;-</span> <span class='no'>errors</span>[<span class='no'>FOCUS_2006_D</span>$<span class='no'>name</span>]
-<span class='no'>f.w.man</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.man</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(f.w.man): Objekt 'f.w.man' nicht gefunden</span></div><div class='input'><span class='no'>f.w.man.irls</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
- <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE, reweight.method = "obs"): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.man.irls</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(f.w.man.irls): Objekt 'f.w.man.irls' nicht gefunden</span></div></pre>
+<span class='no'>f.w.man</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.man</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:28 2019
+#&gt; Date of summary: Thu Jan 31 16:51:28 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 270 model solutions performed in 1.257 s
+#&gt;
+#&gt; Weighting: manual
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.49000 1.33200 96.7800 102.2000
+#&gt; log_k_parent -2.32100 0.03550 -2.3930 -2.2490
+#&gt; log_k_m1 -5.24100 0.21280 -5.6730 -4.8100
+#&gt; f_parent_ilr_1 0.04571 0.08966 -0.1361 0.2275
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.00000 0.5312 -0.09456 -0.3351
+#&gt; log_k_parent 0.53123 1.0000 -0.17800 -0.3360
+#&gt; log_k_m1 -0.09456 -0.1780 1.00000 0.7616
+#&gt; f_parent_ilr_1 -0.33514 -0.3360 0.76156 1.0000
+#&gt;
+#&gt; Residual standard error: 2.628 on 36 degrees of freedom
+#&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 99.490000 74.69 2.221e-41 96.780000 1.022e+02
+#&gt; k_parent 0.098140 28.17 2.012e-26 0.091320 1.055e-01
+#&gt; k_m1 0.005292 4.70 1.873e-05 0.003437 8.148e-03
+#&gt; f_parent_to_m1 0.516200 16.30 1.686e-18 0.452000 5.798e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.400 4 15
+#&gt; parent 6.454 2 7
+#&gt; m1 4.708 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5162
+#&gt; parent_sink 0.4838
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 7.063 23.46
+#&gt; m1 130.971 435.08
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual err
+#&gt; 0 parent 99.46 99.48598 -0.025979 1
+#&gt; 0 parent 102.04 99.48598 2.554021 1
+#&gt; 1 parent 93.50 90.18612 3.313880 1
+#&gt; 1 parent 92.50 90.18612 2.313880 1
+#&gt; 3 parent 63.23 74.11316 -10.883163 1
+#&gt; 3 parent 68.99 74.11316 -5.123163 1
+#&gt; 7 parent 52.32 50.05030 2.269705 1
+#&gt; 7 parent 55.13 50.05030 5.079705 1
+#&gt; 14 parent 27.27 25.17975 2.090250 1
+#&gt; 14 parent 26.64 25.17975 1.460250 1
+#&gt; 21 parent 11.50 12.66765 -1.167654 1
+#&gt; 21 parent 11.64 12.66765 -1.027654 1
+#&gt; 35 parent 2.85 3.20616 -0.356164 1
+#&gt; 35 parent 2.91 3.20616 -0.296164 1
+#&gt; 50 parent 0.69 0.73562 -0.045619 1
+#&gt; 50 parent 0.63 0.73562 -0.105619 1
+#&gt; 75 parent 0.05 0.06326 -0.013256 1
+#&gt; 75 parent 0.06 0.06326 -0.003256 1
+#&gt; 0 m1 0.00 0.00000 0.000000 2
+#&gt; 0 m1 0.00 0.00000 0.000000 2
+#&gt; 1 m1 4.84 4.78729 0.052713 2
+#&gt; 1 m1 5.64 4.78729 0.852713 2
+#&gt; 3 m1 12.91 12.98785 -0.077848 2
+#&gt; 3 m1 12.96 12.98785 -0.027848 2
+#&gt; 7 m1 22.97 24.99695 -2.026946 2
+#&gt; 7 m1 24.47 24.99695 -0.526946 2
+#&gt; 14 m1 41.69 36.66353 5.026472 2
+#&gt; 14 m1 33.21 36.66353 -3.453528 2
+#&gt; 21 m1 44.37 41.65681 2.713186 2
+#&gt; 21 m1 46.44 41.65681 4.783186 2
+#&gt; 35 m1 41.22 43.35031 -2.130314 2
+#&gt; 35 m1 37.95 43.35031 -5.400314 2
+#&gt; 50 m1 41.19 41.25637 -0.066368 2
+#&gt; 50 m1 40.01 41.25637 -1.246368 2
+#&gt; 75 m1 40.09 36.46057 3.629429 2
+#&gt; 75 m1 33.85 36.46057 -2.610571 2
+#&gt; 100 m1 31.04 31.96929 -0.929293 2
+#&gt; 100 m1 33.13 31.96929 1.160707 2
+#&gt; 120 m1 25.15 28.76062 -3.610621 2
+#&gt; 120 m1 33.31 28.76062 4.549379 2</div><div class='input'><span class='no'>f.w.man.irls</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
+ <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f.w.man.irls</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:51:31 2019
+#&gt; Date of summary: Thu Jan 31 16:51:31 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 692 model solutions performed in 3.3 s
+#&gt;
+#&gt; Weighting: manual
+#&gt;
+#&gt; Iterative reweighting with method obs
+#&gt; Final mean squared residuals of observed variables:
+#&gt; parent m1
+#&gt; 11.573406 7.407846
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.67000 1.79200 96.04000 103.300
+#&gt; log_k_parent -2.31200 0.04560 -2.40400 -2.220
+#&gt; log_k_m1 -5.25100 0.12510 -5.50500 -4.998
+#&gt; f_parent_ilr_1 0.03785 0.06318 -0.09027 0.166
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.0000 0.5083 -0.1979 -0.6148
+#&gt; log_k_parent 0.5083 1.0000 -0.3894 -0.6062
+#&gt; log_k_m1 -0.1979 -0.3894 1.0000 0.7417
+#&gt; f_parent_ilr_1 -0.6148 -0.6062 0.7417 1.0000
+#&gt;
+#&gt; Residual standard error: 1.054 on 36 degrees of freedom
+#&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 99.67000 55.630 8.185e-37 96.040000 1.033e+02
+#&gt; k_parent 0.09906 21.930 1.016e-22 0.090310 1.087e-01
+#&gt; k_m1 0.00524 7.996 8.486e-10 0.004066 6.753e-03
+#&gt; f_parent_to_m1 0.51340 23.000 2.039e-23 0.468100 5.584e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.399 4 15
+#&gt; parent 6.466 2 7
+#&gt; m1 4.679 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5134
+#&gt; parent_sink 0.4866
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 6.997 23.24
+#&gt; m1 132.282 439.43
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual err.ini err
+#&gt; 0 parent 99.46 99.67217 -2.122e-01 1 3.402
+#&gt; 0 parent 102.04 99.67217 2.368e+00 1 3.402
+#&gt; 1 parent 93.50 90.27152 3.228e+00 1 3.402
+#&gt; 1 parent 92.50 90.27152 2.228e+00 1 3.402
+#&gt; 3 parent 63.23 74.04648 -1.082e+01 1 3.402
+#&gt; 3 parent 68.99 74.04648 -5.056e+00 1 3.402
+#&gt; 7 parent 52.32 49.82092 2.499e+00 1 3.402
+#&gt; 7 parent 55.13 49.82092 5.309e+00 1 3.402
+#&gt; 14 parent 27.27 24.90288 2.367e+00 1 3.402
+#&gt; 14 parent 26.64 24.90288 1.737e+00 1 3.402
+#&gt; 21 parent 11.50 12.44765 -9.477e-01 1 3.402
+#&gt; 21 parent 11.64 12.44765 -8.077e-01 1 3.402
+#&gt; 35 parent 2.85 3.11002 -2.600e-01 1 3.402
+#&gt; 35 parent 2.91 3.11002 -2.000e-01 1 3.402
+#&gt; 50 parent 0.69 0.70375 -1.375e-02 1 3.402
+#&gt; 50 parent 0.63 0.70375 -7.375e-02 1 3.402
+#&gt; 75 parent 0.05 0.05913 -9.134e-03 1 3.402
+#&gt; 75 parent 0.06 0.05913 8.661e-04 1 3.402
+#&gt; 0 m1 0.00 0.00000 0.000e+00 2 2.722
+#&gt; 0 m1 0.00 0.00000 0.000e+00 2 2.722
+#&gt; 1 m1 4.84 4.81328 2.672e-02 2 2.722
+#&gt; 1 m1 5.64 4.81328 8.267e-01 2 2.722
+#&gt; 3 m1 12.91 13.04779 -1.378e-01 2 2.722
+#&gt; 3 m1 12.96 13.04779 -8.779e-02 2 2.722
+#&gt; 7 m1 22.97 25.07615 -2.106e+00 2 2.722
+#&gt; 7 m1 24.47 25.07615 -6.062e-01 2 2.722
+#&gt; 14 m1 41.69 36.70729 4.983e+00 2 2.722
+#&gt; 14 m1 33.21 36.70729 -3.497e+00 2 2.722
+#&gt; 21 m1 44.37 41.65050 2.719e+00 2 2.722
+#&gt; 21 m1 46.44 41.65050 4.789e+00 2 2.722
+#&gt; 35 m1 41.22 43.28866 -2.069e+00 2 2.722
+#&gt; 35 m1 37.95 43.28866 -5.339e+00 2 2.722
+#&gt; 50 m1 41.19 41.19339 -3.387e-03 2 2.722
+#&gt; 50 m1 40.01 41.19339 -1.183e+00 2 2.722
+#&gt; 75 m1 40.09 36.43820 3.652e+00 2 2.722
+#&gt; 75 m1 33.85 36.43820 -2.588e+00 2 2.722
+#&gt; 100 m1 31.04 31.98971 -9.497e-01 2 2.722
+#&gt; 100 m1 33.13 31.98971 1.140e+00 2 2.722
+#&gt; 120 m1 25.15 28.80897 -3.659e+00 2 2.722
+#&gt; 120 m1 33.31 28.80897 4.501e+00 2 2.722</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index d8114300..84b335f7 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -217,28 +217,51 @@ For the definition of model types and their parameters, the equations given
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span>
-<span class='no'>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='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+<span class='no'>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='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))
+
<span class='co'># One parent compound, one metabolite, both single first order</span>
<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>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <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'>
</div><div class='input'><span class='co'># The above model used to be specified like this, before the advent of mkinsub()</span>
<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='https://www.rdocumentation.org/packages/base/topics/list'>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://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = list(type = "SFO", to = "m1"), m1 = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</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'># Show details of creating the C function</span>
<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; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), verbose = TRUE): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <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 file165a4bc987b7.c 2&gt; file165a4bc987b7.c.err.txt
+#&gt; Program source:
+#&gt; 1: #include &lt;R.h&gt;
+#&gt; 2:
+#&gt; 3:
+#&gt; 4: static double parms [3];
+#&gt; 5: #define k_parent_sink parms[0]
+#&gt; 6: #define k_parent_m1 parms[1]
+#&gt; 7: #define k_m1_sink parms[2]
+#&gt; 8:
+#&gt; 9: void initpar(void (* odeparms)(int *, double *)) {
+#&gt; 10: int N = 3;
+#&gt; 11: odeparms(&amp;N, parms);
+#&gt; 12: }
+#&gt; 13:
+#&gt; 14:
+#&gt; 15: void func ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) {
+#&gt; 16:
+#&gt; 17: f[0] = - k_parent_sink * y[0] - k_parent_m1 * y[0];
+#&gt; 18: f[1] = + k_parent_m1 * y[0] - k_m1_sink * y[1];
+#&gt; 19: }</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'># If we have several parallel metabolites </span>
<span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span>
<span class='no'>m_synth_DFOP_par</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'>"DFOP"</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</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'>M2</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>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("DFOP", c("M1", "M2")), M1 = mkinsub("SFO"), M2 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+
<span class='no'>fit_DFOP_par_c</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_DFOP_par</span>,
<span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>12</span>]]$<span class='no'>data</span>,
- <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(m_synth_DFOP_par, synthetic_data_for_UBA_2014[[12]]$data, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div></pre>
+ <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html
index 28357356..93aa4b41 100644
--- a/docs/reference/mkinparplot.html
+++ b/docs/reference/mkinparplot.html
@@ -151,7 +151,8 @@
<pre class="examples"><div class='input'><span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
<span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"phenol"</span>), <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
<span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"anisole"</span>)),
- <span class='kw'>anisole</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='error'>Error in mkinmod(T245 = mkinsub("SFO", to = c("phenol"), sink = FALSE), phenol = mkinsub("SFO", to = c("anisole")), anisole = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'>mkinparplot</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinparplot(fit): konnte Funktion "mkinparplot" nicht finden</span></div></pre>
+ <span class='kw'>anisole</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'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/subset'>subset</a></span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'>mkinparplot</span>(<span class='no'>fit</span>)</div><div class='img'><img src='mkinparplot-1.png' alt='' width='700' height='433' /></div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html
index c126eec9..c34da511 100644
--- a/docs/reference/mkinpredict.html
+++ b/docs/reference/mkinpredict.html
@@ -208,39 +208,172 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'> <span class='no'>SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>degradinol</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='error'>Error in mkinmod(degradinol = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'> <span class='co'># Compare solution types</span>
+ <pre class="examples"><div class='input'> <span class='no'>SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))
+ <span class='co'># Compare solution types</span>
<span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "deSolve"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</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>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "deSolve", use_compiled = FALSE): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "eigen"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'>
+ <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)</div><div class='output co'>#&gt; time degradinol
+#&gt; 1 0 100.0000000
+#&gt; 2 1 74.0818221
+#&gt; 3 2 54.8811636
+#&gt; 4 3 40.6569660
+#&gt; 5 4 30.1194212
+#&gt; 6 5 22.3130160
+#&gt; 7 6 16.5298888
+#&gt; 8 7 12.2456428
+#&gt; 9 8 9.0717953
+#&gt; 10 9 6.7205513
+#&gt; 11 10 4.9787068
+#&gt; 12 11 3.6883167
+#&gt; 13 12 2.7323722
+#&gt; 14 13 2.0241911
+#&gt; 15 14 1.4995577
+#&gt; 16 15 1.1108997
+#&gt; 17 16 0.8229747
+#&gt; 18 17 0.6096747
+#&gt; 19 18 0.4516581
+#&gt; 20 19 0.3345965
+#&gt; 21 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+ <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)</div><div class='output co'>#&gt; time degradinol
+#&gt; 1 0 100.0000000
+#&gt; 2 1 74.0818221
+#&gt; 3 2 54.8811636
+#&gt; 4 3 40.6569660
+#&gt; 5 4 30.1194212
+#&gt; 6 5 22.3130160
+#&gt; 7 6 16.5298888
+#&gt; 8 7 12.2456428
+#&gt; 9 8 9.0717953
+#&gt; 10 9 6.7205513
+#&gt; 11 10 4.9787068
+#&gt; 12 11 3.6883167
+#&gt; 13 12 2.7323722
+#&gt; 14 13 2.0241911
+#&gt; 15 14 1.4995577
+#&gt; 16 15 1.1108996
+#&gt; 17 16 0.8229747
+#&gt; 18 17 0.6096747
+#&gt; 19 18 0.4516581
+#&gt; 20 19 0.3345965
+#&gt; 21 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</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>)</div><div class='output co'>#&gt; time degradinol
+#&gt; 1 0 100.0000000
+#&gt; 2 1 74.0818221
+#&gt; 3 2 54.8811636
+#&gt; 4 3 40.6569660
+#&gt; 5 4 30.1194212
+#&gt; 6 5 22.3130160
+#&gt; 7 6 16.5298888
+#&gt; 8 7 12.2456428
+#&gt; 9 8 9.0717953
+#&gt; 10 9 6.7205513
+#&gt; 11 10 4.9787068
+#&gt; 12 11 3.6883167
+#&gt; 13 12 2.7323722
+#&gt; 14 13 2.0241911
+#&gt; 15 14 1.4995577
+#&gt; 16 15 1.1108996
+#&gt; 17 16 0.8229747
+#&gt; 18 17 0.6096747
+#&gt; 19 18 0.4516581
+#&gt; 20 19 0.3345965
+#&gt; 21 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+ <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)</div><div class='output co'>#&gt; time degradinol
+#&gt; 1 0 100.0000000
+#&gt; 2 1 74.0818221
+#&gt; 3 2 54.8811636
+#&gt; 4 3 40.6569660
+#&gt; 5 4 30.1194212
+#&gt; 6 5 22.3130160
+#&gt; 7 6 16.5298888
+#&gt; 8 7 12.2456428
+#&gt; 9 8 9.0717953
+#&gt; 10 9 6.7205513
+#&gt; 11 10 4.9787068
+#&gt; 12 11 3.6883167
+#&gt; 13 12 2.7323722
+#&gt; 14 13 2.0241911
+#&gt; 15 14 1.4995577
+#&gt; 16 15 1.1108997
+#&gt; 17 16 0.8229747
+#&gt; 18 17 0.6096747
+#&gt; 19 18 0.4516581
+#&gt; 20 19 0.3345965
+#&gt; 21 20 0.2478752</div><div class='input'>
<span class='co'># Compare integration methods to analytical solution</span>
<span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"lsoda"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "lsoda"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"ode45"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "ode45"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
- <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"rk4"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "rk4"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='co'># rk4 is not as precise here</span>
+ <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; time degradinol
+#&gt; 21 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+ <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"lsoda"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; time degradinol
+#&gt; 21 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+ <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"ode45"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; time degradinol
+#&gt; 21 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+ <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"rk4"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt; time degradinol
+#&gt; 21 20 0.2480043</div><div class='input'> <span class='co'># rk4 is not as precise here</span>
<span class='co'># The number of output times used to make a lot of difference until the</span>
<span class='co'># default for atol was adjusted</span>
<span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>),
- <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>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='fl'>201</span>,]</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), seq(0, 20, by = 0.1)): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>),
- <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>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'>0.01</span>))[<span class='fl'>2001</span>,]</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), seq(0, 20, by = 0.01)): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'>
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>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='fl'>201</span>,]</div><div class='output co'>#&gt; time degradinol
+#&gt; 201 20 0.2478752</div><div class='input'> <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>k_degradinol_sink</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>),
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>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'>0.01</span>))[<span class='fl'>2001</span>,]</div><div class='output co'>#&gt; time degradinol
+#&gt; 2001 20 0.2478752</div><div class='input'>
<span class='co'># Check compiled model versions - they are faster than the eigenvalue based solutions!</span>
<span class='no'>SFO_SFO</span> <span class='kw'>=</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://www.rdocumentation.org/packages/base/topics/list'>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://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = list(type = "SFO", to = "m1"), m1 = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</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='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>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://www.rdocumentation.org/packages/base/topics/c'>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://www.rdocumentation.org/packages/base/topics/seq'>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; <span class='error'>Error in mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "eigen"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0.001</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></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 0.003 </div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>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://www.rdocumentation.org/packages/base/topics/c'>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://www.rdocumentation.org/packages/base/topics/seq'>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; <span class='error'>Error in mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "deSolve"): konnte Funktion "mkinpredict" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></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; 0.002 0.000 0.002 </div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>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://www.rdocumentation.org/packages/base/topics/c'>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://www.rdocumentation.org/packages/base/topics/seq'>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; <span class='error'>Error in mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "deSolve", use_compiled = FALSE): konnte Funktion "mkinpredict" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0</span></div><div class='input'>
+ <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; 0.042 0.000 0.042 </div><div class='input'>
</div><div class='input'> <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='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_C): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/head'>head</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>f</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinpredict(f): konnte Funktion "mkinpredict" nicht finden</span></div><div class='input'> </div></pre>
+ <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; Model cost at call 1 : 552.5739
+#&gt; Model cost at call 3 : 552.5739
+#&gt; Model cost at call 4 : 552.5739
+#&gt; Model cost at call 6 : 279.9345
+#&gt; Model cost at call 7 : 279.9344
+#&gt; Model cost at call 8 : 279.9294
+#&gt; Model cost at call 9 : 279.9294
+#&gt; Model cost at call 12 : 200.3629
+#&gt; Model cost at call 13 : 200.3629
+#&gt; Model cost at call 18 : 197.9039
+#&gt; Model cost at call 23 : 197.9039
+#&gt; Model cost at call 25 : 196.6754
+#&gt; Model cost at call 27 : 196.6754
+#&gt; Model cost at call 32 : 196.5742
+#&gt; Model cost at call 33 : 196.5742
+#&gt; Model cost at call 34 : 196.5742
+#&gt; Model cost at call 38 : 196.5361
+#&gt; Model cost at call 40 : 196.5361
+#&gt; Model cost at call 44 : 196.5336
+#&gt; Model cost at call 45 : 196.5336
+#&gt; Model cost at call 50 : 196.5334
+#&gt; Model cost at call 51 : 196.5334
+#&gt; Model cost at call 52 : 196.5334
+#&gt; Model cost at call 56 : 196.5334
+#&gt; Model cost at call 58 : 196.5334
+#&gt; Model cost at call 59 : 196.5334
+#&gt; Model cost at call 65 : 196.5334
+#&gt; Model cost at call 73 : 196.5334
+#&gt; Model cost at call 78 : 196.5334
+#&gt; Model cost at call 80 : 196.5334
+#&gt; Optimisation by method Port successfully terminated.</div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/head'>head</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>f</span>))</div><div class='output co'>#&gt; time parent m1
+#&gt; 1 0.0 82.49216 0.000000
+#&gt; 2 0.1 80.00563 1.179955
+#&gt; 3 0.2 77.59404 2.312580
+#&gt; 4 0.3 75.25515 3.399419
+#&gt; 5 0.4 72.98675 4.441969
+#&gt; 6 0.5 70.78673 5.441679</div><div class='input'> </div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html
index f85344e2..d088cfc9 100644
--- a/docs/reference/mkinresplot.html
+++ b/docs/reference/mkinresplot.html
@@ -196,7 +196,8 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'><span class='no'>model</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>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</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='error'>Error in mkinfit(model, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinresplot(fit, "m1"): konnte Funktion "mkinresplot" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'><span class='no'>model</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>))</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'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><div class='img'><img src='mkinresplot-1.png' alt='' width='700' height='433' /></div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mkinsub.html b/docs/reference/mkinsub.html
index c615d772..135c0325 100644
--- a/docs/reference/mkinsub.html
+++ b/docs/reference/mkinsub.html
@@ -169,15 +169,15 @@
<pre class="examples"><div class='input'><span class='co'># One parent compound, one metabolite, both single first order.</span>
<span class='no'>SFO_SFO</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://www.rdocumentation.org/packages/base/topics/list'>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://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = list(type = "SFO", to = "m1"), m1 = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</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'># The same model using mkinsub</span>
<span class='no'>SFO_SFO.2</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'>mkinsub</span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
- <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>mkinsub</span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>mkinsub</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'># Now supplying full names</span>
<span class='no'>SFO_SFO.2</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'>mkinsub</span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, <span class='kw'>full_name</span> <span class='kw'>=</span> <span class='st'>"Test compound"</span>),
- <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>mkinsub</span>(<span class='st'>"SFO"</span>, <span class='kw'>full_name</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1", full_name = "Test compound"), m1 = mkinsub("SFO", full_name = "Metabolite M1")): konnte Funktion "mkinmod" nicht finden</span></div></pre>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>mkinsub</span>(<span class='st'>"SFO"</span>, <span class='kw'>full_name</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html
index af0b8f6f..3fa5c9a5 100644
--- a/docs/reference/mmkin.html
+++ b/docs/reference/mmkin.html
@@ -179,22 +179,40 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><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='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='st'>"M2"</span>),
- <span class='kw'>M2</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='error'>Error in mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO", "M2"), M2 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
+ <span class='kw'>M2</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'>m_synth_FOMC_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='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</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='st'>"M2"</span>),
- <span class='kw'>M2</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='error'>Error in mkinmod(parent = mkinsub("FOMC", "M1"), M1 = mkinsub("SFO", "M2"), M2 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
-<span class='no'>models</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>SFO_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_SFO_lin</span>, <span class='kw'>FOMC_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_FOMC_lin</span>)</div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'm_synth_SFO_lin' nicht gefunden</span></div><div class='input'><span class='no'>datasets</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/lapply'>lapply</a></span>(<span class='no'>synthetic_data_for_UBA_2014</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='no'>x</span>$<span class='no'>data</span>)
+ <span class='kw'>M2</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'>models</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>SFO_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_SFO_lin</span>, <span class='kw'>FOMC_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_FOMC_lin</span>)
+<span class='no'>datasets</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/lapply'>lapply</a></span>(<span class='no'>synthetic_data_for_UBA_2014</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='no'>x</span>$<span class='no'>data</span>)
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>datasets</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>)
-<span class='no'>time_default</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(<span class='no'>fits.0</span> <span class='kw'>&lt;-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mmkin(models, datasets, quiet = TRUE): konnte Funktion "mmkin" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0</span></div><div class='input'><span class='no'>time_1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>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>))</div><div class='output co'>#&gt; <span class='error'>Error in mmkin(models, datasets, cores = 1, quiet = TRUE): konnte Funktion "mmkin" nicht finden</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0</span></div><div class='input'>
-<span class='no'>time_default</span></div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'time_default' nicht gefunden</span></div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'time_1' nicht gefunden</span></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; <span class='error'>Error in endpoints(fits.0[["SFO_lin", 2]]): konnte Funktion "endpoints" nicht finden</span></div><div class='input'>
+<span class='no'>time_default</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>system.time</a></span>(<span class='no'>fits.0</span> <span class='kw'>&lt;-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
+<span class='no'>time_1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/system.time'>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; 0.045 0.036 7.264 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#&gt; User System verstrichen
+#&gt; 22.905 0.000 22.919 </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; 0.7340480 0.2659520 0.7505686 0.2494314
+#&gt;
+#&gt; $SFORB
+#&gt; logical(0)
+#&gt;
+#&gt; $distimes
+#&gt; DT50 DT90
+#&gt; parent 0.8777689 2.915885
+#&gt; M1 2.3257453 7.725959
+#&gt; M2 33.7200874 112.015706
+#&gt; </div><div class='input'>
<span class='co'># plot.mkinfit handles rows or columns of mmkin result objects</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ])</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits.0[1, ]): Objekt 'fits.0' nicht gefunden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits.0[1, ], obs_var = c("M1", "M2")): Objekt 'fits.0' nicht gefunden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>])</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits.0[, 1]): Objekt 'fits.0' nicht gefunden</span></div><div class='input'><span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ])</div><div class='img'><img src='mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>))</div><div class='img'><img src='mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>])</div><div class='img'><img src='mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span>
<span class='co'># by plot.mkinfit and can be plotted using plot_sep</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]], <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE): Objekt 'fits.0' nicht gefunden</span></div><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]])</div><div class='output co'>#&gt; <span class='error'>Error in plot_sep(fits.0[[1, 1]]): konnte Funktion "plot_sep" nicht finden</span></div><div class='input'><span class='co'># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]], <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='mmkin-4.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]])
+<span class='co'># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span>
<span class='co'># allow to plot the observed variables separately</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, <span class='fl'>1</span>])</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits.0[1, 1]): Objekt 'fits.0' nicht gefunden</span></div></pre>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, <span class='fl'>1</span>])</div><div class='img'><img src='mmkin-5.png' alt='' width='700' height='433' /></div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html
index 0630c604..ab94d61b 100644
--- a/docs/reference/plot.mkinfit.html
+++ b/docs/reference/plot.mkinfit.html
@@ -260,14 +260,15 @@ plot_sep(fit, sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE, &#8230
<pre class="examples"><div class='input'><span class='co'># One parent compound, one metabolite, both single first order, path from</span>
<span class='co'># parent to sink included, use Levenberg-Marquardt for speed</span>
<span class='no'>SFO_SFO</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'>full</span> <span class='kw'>=</span> <span class='st'>"Parent"</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'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"), m1 = mkinsub("SFO", full = "Metabolite M1")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='no'>fit</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_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, method.modFit = "Marq"): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(fit): Objekt 'fit' nicht gefunden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(fit, show_residuals = TRUE): Objekt 'fit' nicht gefunden</span></div><div class='input'>
+ <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'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</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'>fit</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_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># Show the observed variables separately</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in plot(fit, sep_obs = TRUE, lpos = c("topright", "bottomright")): Objekt 'fit' nicht gefunden</span></div><div class='input'>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># Show the observed variables separately, with residuals</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>),
- <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"), show_errmin = TRUE): Objekt 'fit' nicht gefunden</span></div><div class='input'>
+ <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># The same can be obtained with less typing, using the convenience function plot_sep</span>
-<span class='fu'>plot_sep</span>(<span class='no'>fit</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in plot_sep(fit, lpos = c("topright", "bottomright")): konnte Funktion "plot_sep" nicht finden</span></div></pre>
+<span class='fu'>plot_sep</span>(<span class='no'>fit</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html
index f7afed08..689fd488 100644
--- a/docs/reference/plot.mmkin.html
+++ b/docs/reference/plot.mmkin.html
@@ -187,11 +187,12 @@ If the current plot device is a tikz device,
<pre class="examples"><div class='input'> <span class='co'># Only use one core not to offend CRAN checks, use Levenberg-Marquardt for speed</span>
<span class='no'>fits</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"HS"</span>),
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='st'>"FOCUS B"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_B</span>, <span class='st'>"FOCUS C"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>), <span class='co'># named list for titles</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='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mmkin(c("FOMC", "HS"), list(`FOCUS B` = FOCUS_2006_B, `FOCUS C` = FOCUS_2006_C), cores = 1, quiet = TRUE, method.modFit = "Marq"): konnte Funktion "mmkin" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits[, "FOCUS C"]): Objekt 'fits' nicht gefunden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ])</div><div class='output co'>#&gt; <span class='error'>Error in plot(fits["FOMC", ]): Objekt 'fits' nicht gefunden</span></div><div class='input'>
+ <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='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='img'><img src='plot.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ])</div><div class='img'><img src='plot.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span>
<span class='co'># height should be smaller than the plot width (this is not possible for the html pages</span>
<span class='co'># generated by pkgdown, as far as I know).</span>
- <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span>]) <span class='co'># same as plot(fits[1, 2])</span></div><div class='output co'>#&gt; <span class='error'>Error in plot(fits["FOMC", "FOCUS C"]): Objekt 'fits' nicht gefunden</span></div></pre>
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span>]) <span class='co'># same as plot(fits[1, 2])</span></div><div class='img'><img src='plot.mmkin-3.png' alt='' width='700' height='433' /></div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/print.mkinmod.html b/docs/reference/print.mkinmod.html
index c2cb9e19..0c78b6fb 100644
--- a/docs/reference/print.mkinmod.html
+++ b/docs/reference/print.mkinmod.html
@@ -149,8 +149,22 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <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://www.rdocumentation.org/packages/base/topics/list'>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://www.rdocumentation.org/packages/base/topics/list'>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://www.rdocumentation.org/packages/base/topics/list'>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='error'>Error in mkinmod(parent = list(type = "SFO", to = "M1"), M1 = list(type = "SFO", to = "M2"), M2 = list(type = "SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
- <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='no'>m_synth_SFO_lin</span>)</div><div class='output co'>#&gt; <span class='error'>Error in print(m_synth_SFO_lin): Objekt 'm_synth_SFO_lin' nicht gefunden</span></div></pre>
+ <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='no'>m_synth_SFO_lin</span>)</div><div class='output co'>#&gt; &lt;mkinmod&gt; model generated with
+#&gt; Use of formation fractions $use_of_ff: max
+#&gt; Specification $spec:
+#&gt; $parent
+#&gt; $type: SFO; $to: M1; $sink: TRUE
+#&gt; $M1
+#&gt; $type: SFO; $to: M2; $sink: TRUE
+#&gt; $M2
+#&gt; $type: SFO; $sink: TRUE
+#&gt; Coefficient matrix $coefmat available
+#&gt; Compiled model $cf available
+#&gt; Differential 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</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html
index 0e20e895..d1c89770 100644
--- a/docs/reference/schaefer07_complex_case.html
+++ b/docs/reference/schaefer07_complex_case.html
@@ -155,12 +155,28 @@
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'><span class='no'>data</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkin_wide_to_long.html'>mkin_wide_to_long</a></span>(<span class='no'>schaefer07_complex_case</span>, <span class='kw'>time</span> <span class='kw'>=</span> <span class='st'>"time"</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkin_wide_to_long(schaefer07_complex_case, time = "time"): konnte Funktion "mkin_wide_to_long" nicht finden</span></div><div class='input'><span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
+ <pre class="examples"><div class='input'><span class='no'>data</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkin_wide_to_long.html'>mkin_wide_to_long</a></span>(<span class='no'>schaefer07_complex_case</span>, <span class='kw'>time</span> <span class='kw'>=</span> <span class='st'>"time"</span>)
+<span class='no'>model</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://www.rdocumentation.org/packages/base/topics/list'>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://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"A1"</span>, <span class='st'>"B1"</span>, <span class='st'>"C1"</span>), <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
<span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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'>"A2"</span>),
<span class='kw'>B1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
<span class='kw'>C1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
- <span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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='error'>Error in 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"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'> </div><div class='input'> <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(model, data, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot(fit): Objekt 'fit' nicht gefunden</span></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; <span class='error'>Error in endpoints(fit): konnte Funktion "endpoints" nicht finden</span></div><div class='input'> </div><div class='input'> <span class='co'># Compare with the results obtained in the original publication</span>
+ <span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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'> </div><div class='input'> <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</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='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='schaefer07_complex_case-1.png' alt='' width='700' height='433' /></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_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink
+#&gt; 0.3809621 0.1954665 0.4235714 0.0000000 0.4479674 0.5520326
+#&gt;
+#&gt; $SFORB
+#&gt; logical(0)
+#&gt;
+#&gt; $distimes
+#&gt; DT50 DT90
+#&gt; parent 13.95078 46.34350
+#&gt; A1 49.75342 165.27728
+#&gt; B1 37.26913 123.80536
+#&gt; C1 11.23133 37.30968
+#&gt; A2 28.50591 94.69457
+#&gt; </div><div class='input'> </div><div class='input'> <span class='co'># Compare with the results obtained in the original publication</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/print'>print</a></span>(<span class='no'>schaefer07_complex_results</span>)</div><div class='output co'>#&gt; compound parameter KinGUI ModelMaker deviation
#&gt; 1 parent degradation rate 0.0496 0.0506 2.0
#&gt; 2 parent DT50 13.9900 13.6900 2.2
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index b6f6a5c7..2815eccb 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -206,7 +206,76 @@
<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://www.rdocumentation.org/packages/base/topics/summary'>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; <span class='error'>Error in mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div></pre>
+ <pre class="examples"><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>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: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:52:23 2019
+#&gt; Date of summary: Thu Jan 31 16:52:23 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_parent/dt = - k_parent_sink * parent
+#&gt;
+#&gt; Model predictions using solution type analytical
+#&gt;
+#&gt; Fitted with method Port using 35 model solutions performed in 0.085 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 101.24 state
+#&gt; k_parent_sink 0.10 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 101.240000 -Inf Inf
+#&gt; log_k_parent_sink -2.302585 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; None
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 109.200 4.3910 98.410 119.900
+#&gt; log_k_parent_sink -3.291 0.1152 -3.573 -3.009
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent_sink
+#&gt; parent_0 1.000 0.575
+#&gt; log_k_parent_sink 0.575 1.000
+#&gt;
+#&gt; Residual standard error: 6.08 on 6 degrees of freedom
+#&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_0 109.20000 24.860 1.394e-07 98.41000 119.90000
+#&gt; k_parent_sink 0.03722 8.679 6.457e-05 0.02807 0.04934
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 8.385 2 6
+#&gt; parent 8.385 2 6
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_sink 1
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 18.62 61.87
+#&gt;
+#&gt; Data:
+#&gt; time variable observed predicted residual
+#&gt; 0 parent 101.24 109.153 -7.9132
+#&gt; 3 parent 99.27 97.622 1.6484
+#&gt; 7 parent 90.11 84.119 5.9913
+#&gt; 14 parent 72.19 64.826 7.3641
+#&gt; 30 parent 29.71 35.738 -6.0283
+#&gt; 62 parent 5.98 10.862 -4.8818
+#&gt; 90 parent 1.54 3.831 -2.2911
+#&gt; 118 parent 0.39 1.351 -0.9613</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html
index 49e70681..1999de8d 100644
--- a/docs/reference/test_data_from_UBA_2014.html
+++ b/docs/reference/test_data_from_UBA_2014.html
@@ -155,16 +155,56 @@
<span class='co'># large parameter correlations, among other reasons (e.g. the adequacy of the</span>
<span class='co'># model).</span>
<span class='no'>m_ws</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent_w</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_s"</span>),
- <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent_w = mkinsub("SFO", "parent_s"), parent_s = mkinsub("SFO", "parent_w")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'> <span class='no'>f_river</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_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>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(m_ws, test_data_from_UBA_2014[[1]]$data, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>)</div><div class='output co'>#&gt; <span class='error'>Error in plot_sep(f_river): konnte Funktion "plot_sep" nicht finden</span></div><div class='input'>
- <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt; <span class='error'>Error in summary(f_river): Objekt 'f_river' nicht gefunden</span></div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinerrmin(f_river): konnte Funktion "mkinerrmin" nicht finden</span></div><div class='input'>
+ <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</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_river</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+ <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>)</div><div class='img'><img src='test_data_from_UBA_2014-1.png' alt='' width='700' height='433' /></div><div class='input'>
+ <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt; Estimate se_notrans t value Pr(&gt;t) Lower
+#&gt; parent_w_0 9.598567e+01 2.33959800 4.102657e+01 9.568967e-19 NA
+#&gt; k_parent_w_sink 3.603743e-01 0.03497750 1.030303e+01 4.989002e-09 NA
+#&gt; k_parent_w_parent_s 6.031371e-02 0.01746024 3.454346e+00 1.514723e-03 NA
+#&gt; k_parent_s_sink 5.108539e-11 0.10382001 4.920572e-10 5.000000e-01 NA
+#&gt; k_parent_s_parent_w 7.419672e-02 0.11338240 6.543936e-01 2.608069e-01 NA
+#&gt; Upper
+#&gt; parent_w_0 NA
+#&gt; k_parent_w_sink NA
+#&gt; k_parent_w_parent_s NA
+#&gt; k_parent_s_sink NA
+#&gt; k_parent_s_parent_w NA</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>)</div><div class='output co'>#&gt; err.min n.optim df
+#&gt; All data 0.09246946 5 6
+#&gt; parent_w 0.06377096 3 3
+#&gt; parent_s 0.20882324 2 3</div><div class='input'>
<span class='co'># This is the evaluation used for the validation of software packages</span>
<span class='co'># in the expertise from 2014</span>
<span class='no'>m_soil</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='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</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='st'>"M3"</span>),
<span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>),
<span class='kw'>M3</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='error'>Error in mkinmod(parent = mkinsub("SFO", c("M1", "M2")), M1 = mkinsub("SFO", "M3"), M2 = mkinsub("SFO", "M3"), M3 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
- <span class='no'>f_soil</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_soil</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>3</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(m_soil, test_data_from_UBA_2014[[3]]$data, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in plot_sep(f_soil, lpos = c("topright", "topright", "topright", "bottomright")): konnte Funktion "plot_sep" nicht finden</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt; <span class='error'>Error in summary(f_soil): Objekt 'f_soil' nicht gefunden</span></div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinerrmin(f_soil): konnte Funktion "mkinerrmin" nicht finden</span></div><div class='input'> </div></pre>
+ <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+ <span class='no'>f_soil</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_soil</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>3</span>]]$<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'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='img'><img src='test_data_from_UBA_2014-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt; Estimate se_notrans t value Pr(&gt;t) Lower
+#&gt; parent_0 76.55425584 0.943443794 81.1434198 4.422336e-30 74.602593773
+#&gt; k_parent 0.12081956 0.004815515 25.0896457 1.639665e-18 0.111257517
+#&gt; k_M1 0.84258651 0.930121548 0.9058886 1.871938e-01 0.085876516
+#&gt; k_M2 0.04210878 0.013729902 3.0669396 2.729137e-03 0.021450630
+#&gt; k_M3 0.01122919 0.008044865 1.3958206 8.804912e-02 0.002550984
+#&gt; f_parent_to_M1 0.32240199 0.278620579 1.1571363 1.295467e-01 NA
+#&gt; f_parent_to_M2 0.16099854 0.030548888 5.2701931 1.196190e-05 NA
+#&gt; f_M1_to_M3 0.27921500 0.314732842 0.8871492 1.920908e-01 0.015016983
+#&gt; f_M2_to_M3 0.55641333 0.650246995 0.8556954 2.004966e-01 0.005360555
+#&gt; Upper
+#&gt; parent_0 78.50591790
+#&gt; k_parent 0.13120341
+#&gt; k_M1 8.26712661
+#&gt; k_M2 0.08266188
+#&gt; k_M3 0.04942981
+#&gt; f_parent_to_M1 NA
+#&gt; f_parent_to_M2 NA
+#&gt; f_M1_to_M3 0.90777163
+#&gt; f_M2_to_M3 0.99658633</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>)</div><div class='output co'>#&gt; err.min n.optim df
+#&gt; All data 0.09649963 9 20
+#&gt; parent 0.04721283 2 6
+#&gt; M1 0.26551209 2 5
+#&gt; M2 0.20327575 2 5
+#&gt; M3 0.05196549 3 4</div><div class='input'> </div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html
index b9d96b1e..d3c20ed8 100644
--- a/docs/reference/transform_odeparms.html
+++ b/docs/reference/transform_odeparms.html
@@ -198,24 +198,320 @@ The transformation of sets of formation fractions is fragile, as it supposes
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>SFO_SFO</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://www.rdocumentation.org/packages/base/topics/list'>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'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
- <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='error'>Error in mkinmod(parent = list(type = "SFO", to = "m1", sink = TRUE), m1 = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
-<span class='no'>fit</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_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) <span class='co'># See transformed and backtransformed parameters</span></div><div class='output co'>#&gt; <span class='error'>Error in summary(fit, data = FALSE): Objekt 'fit' nicht gefunden</span></div><div class='input'>
-</div><div class='input'><span class='no'>fit.2</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_D</span>, <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.2</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit.2, data = FALSE): Objekt 'fit.2' nicht gefunden</span></div><div class='input'>
-<span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='no'>fit</span>$<span class='no'>start</span>$<span class='no'>value</span></div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'fit' nicht gefunden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>initials</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colnames'>rownames</a></span>(<span class='no'>fit</span>$<span class='no'>start</span>)</div><div class='output co'>#&gt; <span class='error'>Error in rownames(fit$start): Objekt 'fit' nicht gefunden</span></div><div class='input'><span class='no'>transformed</span> <span class='kw'>&lt;-</span> <span class='no'>fit</span>$<span class='no'>start_transformed</span>$<span class='no'>value</span></div><div class='output co'>#&gt; <span class='error'>Error in eval(expr, envir, enclos): Objekt 'fit' nicht gefunden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>transformed</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colnames'>rownames</a></span>(<span class='no'>fit</span>$<span class='no'>start_transformed</span>)</div><div class='output co'>#&gt; <span class='error'>Error in rownames(fit$start_transformed): Objekt 'fit' nicht gefunden</span></div><div class='input'><span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#&gt; <span class='error'>Error in transform_odeparms(initials, SFO_SFO): konnte Funktion "transform_odeparms" nicht finden</span></div><div class='input'><span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#&gt; <span class='error'>Error in backtransform_odeparms(transformed, SFO_SFO): konnte Funktion "backtransform_odeparms" nicht finden</span></div><div class='input'>
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</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='no'>fit</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_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) <span class='co'># See transformed and backtransformed parameters</span></div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:52:31 2019
+#&gt; Date of summary: Thu Jan 31 16:52:31 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
+#&gt; d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
+#&gt;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 153 model solutions performed in 0.703 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent_sink 0.1000 deparm
+#&gt; k_parent_m1 0.1001 deparm
+#&gt; k_m1_sink 0.1002 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent_sink -2.302585 -Inf Inf
+#&gt; log_k_parent_m1 -2.301586 -Inf Inf
+#&gt; log_k_m1_sink -2.300587 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.600 1.61400 96.330 102.900
+#&gt; log_k_parent_sink -3.038 0.07826 -3.197 -2.879
+#&gt; log_k_parent_m1 -2.980 0.04124 -3.064 -2.897
+#&gt; log_k_m1_sink -5.248 0.13610 -5.523 -4.972
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
+#&gt; parent_0 1.00000 0.6075 -0.06625 -0.1701
+#&gt; log_k_parent_sink 0.60752 1.0000 -0.08740 -0.6253
+#&gt; log_k_parent_m1 -0.06625 -0.0874 1.00000 0.4716
+#&gt; log_k_m1_sink -0.17006 -0.6253 0.47164 1.0000
+#&gt;
+#&gt; Residual standard error: 3.211 on 36 degrees of freedom
+#&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 99.600000 61.720 2.024e-38 96.330000 1.029e+02
+#&gt; k_parent_sink 0.047920 12.780 3.050e-15 0.040890 5.616e-02
+#&gt; k_parent_m1 0.050780 24.250 3.407e-24 0.046700 5.521e-02
+#&gt; k_m1_sink 0.005261 7.349 5.758e-09 0.003992 6.933e-03
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.398 4 15
+#&gt; parent 6.827 3 6
+#&gt; m1 4.490 1 9
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_sink 0.4855
+#&gt; parent_m1 0.5145
+#&gt; m1_sink 1.0000
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 7.023 23.33
+#&gt; m1 131.761 437.70</div><div class='input'>
+</div><div class='input'><span class='no'>fit.2</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_D</span>, <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.2</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:52:33 2019
+#&gt; Date of summary: Thu Jan 31 16:52:33 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
+#&gt; d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
+#&gt;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 350 model solutions performed in 1.597 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent_sink 0.1000 deparm
+#&gt; k_parent_m1 0.1001 deparm
+#&gt; k_m1_sink 0.1002 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.7500 -Inf Inf
+#&gt; k_parent_sink 0.1000 0 Inf
+#&gt; k_parent_m1 0.1001 0 Inf
+#&gt; k_m1_sink 0.1002 0 Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.600000 1.6140000 96.330000 1.029e+02
+#&gt; k_parent_sink 0.047920 0.0037500 0.040310 5.553e-02
+#&gt; k_parent_m1 0.050780 0.0020940 0.046530 5.502e-02
+#&gt; k_m1_sink 0.005261 0.0007159 0.003809 6.713e-03
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 k_parent_sink k_parent_m1 k_m1_sink
+#&gt; parent_0 1.00000 0.6075 -0.06625 -0.1701
+#&gt; k_parent_sink 0.60752 1.0000 -0.08740 -0.6253
+#&gt; k_parent_m1 -0.06625 -0.0874 1.00000 0.4716
+#&gt; k_m1_sink -0.17006 -0.6253 0.47164 1.0000
+#&gt;
+#&gt; Residual standard error: 3.211 on 36 degrees of freedom
+#&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 99.600000 61.720 2.024e-38 96.330000 1.029e+02
+#&gt; k_parent_sink 0.047920 12.780 3.050e-15 0.040310 5.553e-02
+#&gt; k_parent_m1 0.050780 24.250 3.407e-24 0.046530 5.502e-02
+#&gt; k_m1_sink 0.005261 7.349 5.758e-09 0.003809 6.713e-03
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.398 4 15
+#&gt; parent 6.827 3 6
+#&gt; m1 4.490 1 9
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_sink 0.4855
+#&gt; parent_m1 0.5145
+#&gt; m1_sink 1.0000
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 7.023 23.33
+#&gt; m1 131.761 437.70</div><div class='input'>
+<span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='no'>fit</span>$<span class='no'>start</span>$<span class='no'>value</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>initials</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colnames'>rownames</a></span>(<span class='no'>fit</span>$<span class='no'>start</span>)
+<span class='no'>transformed</span> <span class='kw'>&lt;-</span> <span class='no'>fit</span>$<span class='no'>start_transformed</span>$<span class='no'>value</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/names'>names</a></span>(<span class='no'>transformed</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colnames'>rownames</a></span>(<span class='no'>fit</span>$<span class='no'>start_transformed</span>)
+<span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#&gt; parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
+#&gt; 100.750000 -2.302585 -2.301586 -2.300587 </div><div class='input'><span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#&gt; parent_0 k_parent_sink k_parent_m1 k_m1_sink
+#&gt; 100.7500 0.1000 0.1001 0.1002 </div><div class='input'>
</div><div class='input'><span class='co'># The case of formation fractions</span>
<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='https://www.rdocumentation.org/packages/base/topics/list'>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'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
<span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>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='error'>Error in mkinmod(parent = list(type = "SFO", to = "m1", sink = TRUE), m1 = list(type = "SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
-<span class='no'>fit.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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='error'>Error in mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.ff</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit.ff, data = FALSE): Objekt 'fit.ff' nicht gefunden</span></div><div class='input'><span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"f_parent_to_m1"</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)
-<span class='no'>transformed</span> <span class='kw'>&lt;-</span> <span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO.ff</span>)</div><div class='output co'>#&gt; <span class='error'>Error in transform_odeparms(initials, SFO_SFO.ff): konnte Funktion "transform_odeparms" nicht finden</span></div><div class='input'><span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO.ff</span>)</div><div class='output co'>#&gt; <span class='error'>Error in backtransform_odeparms(transformed, SFO_SFO.ff): konnte Funktion "backtransform_odeparms" nicht finden</span></div><div class='input'>
+ <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'>fit.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></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>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.ff</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:52:34 2019
+#&gt; Date of summary: Thu Jan 31 16:52:34 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;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 186 model solutions performed in 0.898 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt; f_parent_to_m1 0.5000 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt; f_parent_ilr_1 0.000000 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 99.60000 1.61400 96.3300 102.9000
+#&gt; log_k_parent -2.31600 0.04187 -2.4010 -2.2310
+#&gt; log_k_m1 -5.24800 0.13610 -5.5230 -4.9720
+#&gt; f_parent_ilr_1 0.04096 0.06477 -0.0904 0.1723
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1 f_parent_ilr_1
+#&gt; parent_0 1.0000 0.5178 -0.1701 -0.5489
+#&gt; log_k_parent 0.5178 1.0000 -0.3285 -0.5451
+#&gt; log_k_m1 -0.1701 -0.3285 1.0000 0.7466
+#&gt; f_parent_ilr_1 -0.5489 -0.5451 0.7466 1.0000
+#&gt;
+#&gt; Residual standard error: 3.211 on 36 degrees of freedom
+#&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 99.600000 61.720 2.024e-38 96.330000 1.029e+02
+#&gt; k_parent 0.098700 23.880 5.700e-24 0.090660 1.074e-01
+#&gt; k_m1 0.005261 7.349 5.758e-09 0.003992 6.933e-03
+#&gt; f_parent_to_m1 0.514500 22.490 4.375e-23 0.468100 5.606e-01
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 6.398 4 15
+#&gt; parent 6.459 2 7
+#&gt; m1 4.690 2 8
+#&gt;
+#&gt; Resulting formation fractions:
+#&gt; ff
+#&gt; parent_m1 0.5145
+#&gt; parent_sink 0.4855
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 7.023 23.33
+#&gt; m1 131.761 437.70</div><div class='input'><span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"f_parent_to_m1"</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)
+<span class='no'>transformed</span> <span class='kw'>&lt;-</span> <span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO.ff</span>)
+<span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO.ff</span>)</div><div class='output co'>#&gt; f_parent_to_m1
+#&gt; 0.5 </div><div class='input'>
<span class='co'># And without sink</span>
<span class='no'>SFO_SFO.ff.2</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://www.rdocumentation.org/packages/base/topics/list'>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'>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://www.rdocumentation.org/packages/base/topics/list'>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='error'>Error in mkinmod(parent = list(type = "SFO", to = "m1", sink = FALSE), m1 = list(type = "SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden</span></div><div class='input'>
-
-<span class='no'>fit.ff.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff.2</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='error'>Error in mkinfit(SFO_SFO.ff.2, FOCUS_2006_D, quiet = TRUE): konnte Funktion "mkinfit" nicht finden</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.ff.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit.ff.2, data = FALSE): Objekt 'fit.ff.2' nicht gefunden</span></div></pre>
+ <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'>fit.ff.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff.2</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>fit.ff.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.47.6
+#&gt; R version used for fitting: 3.5.2
+#&gt; Date of fit: Thu Jan 31 16:52:35 2019
+#&gt; Date of summary: Thu Jan 31 16:52:35 2019
+#&gt;
+#&gt; Equations:
+#&gt; d_parent/dt = - k_parent * parent
+#&gt; d_m1/dt = + k_parent * parent - k_m1 * m1
+#&gt;
+#&gt; Model predictions using solution type deSolve
+#&gt;
+#&gt; Fitted with method Port using 104 model solutions performed in 0.482 s
+#&gt;
+#&gt; Weighting: none
+#&gt;
+#&gt; Starting values for parameters to be optimised:
+#&gt; value type
+#&gt; parent_0 100.7500 state
+#&gt; k_parent 0.1000 deparm
+#&gt; k_m1 0.1001 deparm
+#&gt;
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt; value lower upper
+#&gt; parent_0 100.750000 -Inf Inf
+#&gt; log_k_parent -2.302585 -Inf Inf
+#&gt; log_k_m1 -2.301586 -Inf Inf
+#&gt;
+#&gt; Fixed parameter values:
+#&gt; value type
+#&gt; m1_0 0 state
+#&gt;
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt; Estimate Std. Error Lower Upper
+#&gt; parent_0 84.790 2.96500 78.78 90.800
+#&gt; log_k_parent -2.756 0.08088 -2.92 -2.593
+#&gt; log_k_m1 -4.214 0.11150 -4.44 -3.988
+#&gt;
+#&gt; Parameter correlation:
+#&gt; parent_0 log_k_parent log_k_m1
+#&gt; parent_0 1.0000 0.11058 0.46156
+#&gt; log_k_parent 0.1106 1.00000 0.06274
+#&gt; log_k_m1 0.4616 0.06274 1.00000
+#&gt;
+#&gt; Residual standard error: 8.333 on 37 degrees of freedom
+#&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 84.79000 28.600 3.939e-27 78.78000 90.80000
+#&gt; k_parent 0.06352 12.360 5.237e-15 0.05392 0.07483
+#&gt; k_m1 0.01478 8.966 4.114e-11 0.01179 0.01853
+#&gt;
+#&gt; Chi2 error levels in percent:
+#&gt; err.min n.optim df
+#&gt; All data 19.66 3 16
+#&gt; parent 17.56 2 7
+#&gt; m1 18.71 1 9
+#&gt;
+#&gt; Estimated disappearance times:
+#&gt; DT50 DT90
+#&gt; parent 10.91 36.25
+#&gt; m1 46.89 155.75</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
diff --git a/inst/examples/mkinds.R b/inst/examples/mkinds.R
deleted file mode 100644
index 6c5d2cfc..00000000
--- a/inst/examples/mkinds.R
+++ /dev/null
@@ -1 +0,0 @@
-mds <- mkinds$new("FOCUS A", FOCUS_2006_A)
diff --git a/vignettes/FOCUS_D.Rmd b/vignettes/FOCUS_D.Rmd
index 037dd854..02c2c467 100644
--- a/vignettes/FOCUS_D.Rmd
+++ b/vignettes/FOCUS_D.Rmd
@@ -3,7 +3,7 @@ title: Example evaluation of FOCUS Example Dataset D
author: Johannes Ranke
date: "`r Sys.Date()`"
output:
- html_vignette:
+rmarkdown::html_vignette:
mathjax: null
vignette: >
%\VignetteIndexEntry{Example evaluation of FOCUS Example Dataset D}
@@ -11,20 +11,21 @@ vignette: >
%\VignetteEncoding{UTF-8}
---
-```{r, include = FALSE}
+```{r, include = FALSE, message = FALSE}
library(knitr)
-opts_chunk$set(tidy = FALSE, cache = TRUE)
+opts_chunk$set(tidy = FALSE, cache = FALSE)
+library(mkin)
```
This is just a very simple vignette showing how to fit a degradation model for a parent
compound with one transformation product using `mkin`. After loading the
-library we look a the data. We have observed concentrations in the column named
+library we look at the data. We have observed concentrations in the column named
`value` at the times specified in column `time` for the two observed variables
named `parent` and `m1`.
```{r data}
-library("mkin", quietly = TRUE)
+library(mkin, quietly = TRUE)
print(FOCUS_2006_D)
```
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index bfbe2f7e..0507740f 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -8,75 +8,378 @@
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
-<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="author" content="Johannes Ranke" />
-<meta name="date" content="2018-07-17" />
+<meta name="date" content="2019-01-31" />
<title>Example evaluation of FOCUS Example Dataset D</title>
+<script>/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
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yr6EIj+LzNj/mfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h/C/PkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn/pdpgHKNGrGIdkRK+KPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho+EIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA+q/j9m3LM/O7WJka4tSidVCjsvo2lQ/2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3/5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho/bcwQdeboqfQartuU3CsCf+cXkgYAqp/0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid/NIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A+TRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC+JE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW/WDHA60cYFaI/PjpzquUqdaYGcIq+mLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC+1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A+P/oFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E/vwOiKxRtCWsDM+eTHUrmwrCK5BIfMzGkD+0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3/kFutpQGNc3pCR7gvC4sgwbupDu3DyEN+W6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc+h1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0+bC5zgpGz7Io+mjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO/ENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn+3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif+pZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx/k3QgnAFSq27/2i4GEBA+UvTJKK/9eISNvG46Em5RZfjTYLdeD8kdXHyrwId/DQZUaMCY4gGbke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS/2ToPjqkaq62/7WFG8advGlRRqxB9diP07JrXowKR9tpRa+jGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq+nsp3YMuXt/GkXxLx/P6+ZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar+gMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud+YlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl/zh575R5rsrmRnKAzq4POFdgbYBuEviM4+LVC15ssLNFghbTtHWerS1hDt5s4qkLUha/qpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI+yjEldJfSo4y0QhG4i4IwkRFGcjWY8+EzgYYJUK7BXQksLxAww/YYWBMhJILB9e8ePEJ4OP7z+4/wOQDl64iOYDp26DaONPxpKtBxq/aTzRGarm3VkPYTLJKx6Z/Mw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ+lPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k/UuGkNpP1DBI5ch/EehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv+J41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI+HYexTUevLUeta4/DqKrbMVS+Yqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ++KkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9/Wx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k/90B8+yRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB/mQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go/n6j0cS+a2gEU8gIHJ+BwfgZX4GL+Bd/gW34FZ+BS/gUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh/F062yJ7AAAAEDAWAAABWhJ+KPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg) 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g5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1+x1SXwLpFi53b+aHRYo/IrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f+nAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g/VRdpkkyVpD7jnmax88QwDvg/66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb+YFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb/d5gqmNaqJZSO1l6PVRy0nZIj/45EetjLguh1rLqR+SK0hO6NrsqcNX8zoUdjQYDJ7tb4os6+i+Y0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY/gZmE9iuP4V5v7CSR/RThaHLh+UeBkXwU6BC7lGOevK65udTv+tS/PfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso/c/1K9aLE12fMWLV1y1D9ua8lyJdWXr/bG+noCFutf/mLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq+PEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj+nzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3++fcnT49Qe+kBs+wdsMZgPXMp3U5S958snPP/EE7bvkOPCuTUDTUQ/UzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk/seDVWsvDWXXXsyn/4wfpXc2V3/Qxli3jl/5hj/83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3/+2TJrzLlkFz+ip3qCHKf6eqW02QJLjBYuuj4sobhCWqa/YHGEHpcnumuWSOhxeaL7sOakNR6vvmo+YcfFA8UFXEPZf9UjyudIOyNwx/i90DdsujS/FX2UAwvWSVK4NxaMhAGw3oowp/uc8CTi7D2rBgZWwb/60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E+o2t1nePTn8TUmxz/A3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD+qJopELrl4S6cJNRtEAsLP/OdvnJq0Wo0GolY2Et9VFB2Kf+4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam+bWmUJZfNn5ozKV5Pmmq8PF/jVY+2Tlk2M2RzSXKjmbQ4RZcQavEYrN/9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd+SyX/Cyf0Vxjd2R9NmsunnXYa8xGHzn+sSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV+D5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H/lpZBicglCPN7WOfJkcHqc3ElWqvvz/1E6bIQrG+tz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw+WSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM+wEW0El9dM/ueVRWIsXwe43R7SgbVZqrnqoJ1X/kuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM+foaCcwgaoqZUtmodV3p+X7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z+O4fYIbvZVt4EUcJEKOhHeincWqONw+q6w5Go+WGOSR7LhKV+KBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx+SyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq/qlFo1v+PTmTi3E/glC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO+YOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr+H1jR7km9vcvqN9657vffefkv+fKxge1X+7RdjYUPIESN7gTvRkB/RMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F+UX4+WKS4iBTP0IWxGtZgOdMaV4KTayqHQF/VihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g/qRCuy3vmQ3W9Vu8qbVbP+SzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB+LopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR/ut5Ug2jXAaT96+9RH/FToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye/78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard/nGAVN8alecnFdgu5Gbd5DJTL+hHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf+9Gp+VnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687+P3cx6tcAeNpjYGRgYGBk8s0/zBIfz2/zlUGeZQNQhOFCWfF0GP0/8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV+d8KJgP/XWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF++2W/nZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA+wT8L5nOdMaV0AuMJ+icRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn/AtWZyj3W0HjEXitOc/dIdbetPdFTZ+P6t+X7xU0/k6GJtOe1/B3arN0/pmz1J4UZc+D6ExwjD7vioeGd5HvhvU+R+DZcGZ6YBPNfAi0G97iBPwFXqph2cW8+D7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb/GOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY+5hPsLCwWLEksSyiOUOawzrLrYiti/sCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k/klqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R/aZnJ5cmdwB+ST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R/6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S/6Gnopekt0ruhz6fvpl+nv0n/h4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ/LA1sn1jZ2XXY3fFXsM+z36V/S8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l/8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf/d6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU/hM9jYWPgIFkYfwd6nsDD+d1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui+ncOr7D02y6/BTCrP/m+b5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU/exhxztMozlosTpMH6ZPge0L+OKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo//G7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq/ZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr/PW70/HO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8/3JOUnqAyR1d/cCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs/NdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxG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+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+ "use strict";
+ $.fn.rmarkdownStickyTabs = function() {
+ var context = this;
+ // Show the tab corresponding with the hash in the URL, or the first tab
+ var showStuffFromHash = function() {
+ var hash = window.location.hash;
+ var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+ var $selector = $(selector, context);
+ if($selector.data('toggle') === "tab") {
+ $selector.tab('show');
+ // walk up the ancestors of this element, show any hidden tabs
+ $selector.parents('.section.tabset').each(function(i, elm) {
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+ });
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+ // Set the correct tab when a user uses their back/forward button
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+ // set active tab
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+ var active = $(tabContent.children('div.section')[activeTab]);
+ active.addClass('active');
+ if (fade)
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+
+ if (tabset.hasClass("tabset-sticky"))
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+ }
+
+ // convert section divs with the .tabset class to tabsets
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<h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1>
<h4 class="author"><em>Johannes Ranke</em></h4>
-<h4 class="date"><em>2018-07-17</em></h4>
+<h4 class="date"><em>2019-01-31</em></h4>
+</div>
-<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)
-<span class="kw">print</span>(FOCUS_<span class="dv">2006</span>_D)</code></pre></div>
+<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look at the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p>
+<pre class="r"><code>library(mkin, quietly = TRUE)
+print(FOCUS_2006_D)</code></pre>
<pre><code>## name time value
## 1 parent 0 99.46
## 2 parent 0 102.04
@@ -124,27 +427,27 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf
## 44 m1 120 33.31</code></pre>
<p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p>
<p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">SFO_SFO &lt;-<span class="st"> </span><span class="kw">mkinmod</span>(<span class="dt">parent =</span> <span class="kw">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;m1&quot;</span>), <span class="dt">m1 =</span> <span class="kw">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>))</code></pre></div>
+<pre class="r"><code>SFO_SFO &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;m1&quot;), m1 = mkinsub(&quot;SFO&quot;))</code></pre>
<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(SFO_SFO<span class="op">$</span>diffs)</code></pre></div>
+<pre class="r"><code>print(SFO_SFO$diffs)</code></pre>
<pre><code>## parent
## &quot;d_parent = - k_parent_sink * parent - k_parent_m1 * parent&quot;
## m1
## &quot;d_m1 = + k_parent_m1 * parent - k_m1_sink * m1&quot;</code></pre>
<p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit &lt;-<span class="st"> </span><span class="kw">mkinfit</span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div>
+<pre class="r"><code>fit &lt;- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</code></pre>
<p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot_sep</span>(fit, <span class="dt">lpos =</span> <span class="kw">c</span>(<span class="st">&quot;topright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>))</code></pre></div>
-<p><img 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" /><!-- --></p>
+<pre class="r"><code>plot_sep(fit, lpos = c(&quot;topright&quot;, &quot;bottomright&quot;))</code></pre>
+<p><img 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width="768" /></p>
<p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mkinparplot</span>(fit)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<pre class="r"><code>mkinparplot(fit)</code></pre>
+<p><img src="data:image/png;base64,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" width="768" /></p>
<p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(fit)</code></pre></div>
-<pre><code>## mkin version used for fitting: 0.9.47.1
-## R version used for fitting: 3.5.1
-## Date of fit: Tue Jul 17 15:54:19 2018
-## Date of summary: Tue Jul 17 15:54:19 2018
+<pre class="r"><code>summary(fit)</code></pre>
+<pre><code>## mkin version used for fitting: 0.9.47.5
+## R version used for fitting: 3.5.2
+## Date of fit: Thu Jan 31 16:45:24 2019
+## Date of summary: Thu Jan 31 16:45:24 2019
##
## Equations:
## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -152,7 +455,7 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf
##
## Model predictions using solution type deSolve
##
-## Fitted with method Port using 153 model solutions performed in 0.658 s
+## Fitted with method Port using 153 model solutions performed in 0.684 s
##
## Weighting: none
##
@@ -263,5 +566,30 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf
+</div>
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