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-rw-r--r--vignettes/mkin.Rmd4
-rw-r--r--vignettes/mkin.html8
2 files changed, 6 insertions, 6 deletions
diff --git a/vignettes/mkin.Rmd b/vignettes/mkin.Rmd
index 78fd098f..acca0e44 100644
--- a/vignettes/mkin.Rmd
+++ b/vignettes/mkin.Rmd
@@ -128,7 +128,7 @@ up to six metabolites in a flexible arrangement, but does not support
back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.
KinGUI offers an even more flexible widget for specifying complex kinetic
-models. Back-reactions (non-instanteneous equilibria) were supported early on,
+models. Back-reactions (non-instantaneous equilibria) were supported early on,
but until 2014, only simple first-order models could be specified for
transformation products. Starting with KinGUII version 2.1, biphasic modelling
of metabolites was also available in KinGUII.
@@ -192,7 +192,7 @@ confidence intervals.
In the first attempt at providing improved parameter confidence intervals
introduced to `mkin` in 2013, confidence intervals obtained from
FME on the transformed parameters were simply all backtransformed one by one
-to yield asymetric confidence intervals for the backtransformed parameters.
+to yield asymmetric confidence intervals for the backtransformed parameters.
However, while there is a 1:1 relation between the rate constants in the model
and the transformed parameters fitted in the model, the parameters obtained by the
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 2ba360f4..28b3fa16 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -11,7 +11,7 @@
<meta name="author" content="Johannes Ranke" />
-<meta name="date" content="2020-05-11" />
+<meta name="date" content="2020-05-12" />
<title>Introduction to mkin</title>
@@ -1583,7 +1583,7 @@ div.tocify {
<h1 class="title toc-ignore">Introduction to mkin</h1>
<h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2020-05-11</h4>
+<h4 class="date">2020-05-12</h4>
</div>
@@ -1635,7 +1635,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c(&quot;topright&quot;, &quot;bottomright&quot;,
<h2>Derived software tools</h2>
<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p>
<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</p>
-<p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instanteneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p>
+<p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p>
<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p>
<p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p>
</div>
@@ -1653,7 +1653,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c(&quot;topright&quot;, &quot;bottomright&quot;,
<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.</p>
<div id="confidence-intervals-based-on-transformed-parameters" class="section level2">
<h2>Confidence intervals based on transformed parameters</h2>
-<p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymetric confidence intervals for the backtransformed parameters.</p>
+<p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymmetric confidence intervals for the backtransformed parameters.</p>
<p>However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.</p>
<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.</p>
<p>The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.</p>

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