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authorJohannes Ranke <jranke@uni-bremen.de>2016-06-28 00:44:48 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2016-06-28 01:30:00 +0200
commitef7ccf68e086bac3e75858bd4b97e3a2c6872aa1 (patch)
treee1b9e55736b0961843e84c60caeb9117e1c35c6d /inst/web/add_err.html
parent4672c2b3fd6c22e63b00ee5038998dd68a885c25 (diff)
Avoid multicore fits in vignettes for passing CRAN checks
Also a small fix to the FOCUS Z vignette, and an update of the static documentation generated by staticdocs.
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+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8">
+<title>add_err. mkin 0.9.43</title>
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="author" content="
+ Johannes Ranke
+">
+
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+ }
+ });
+</script>
+<script type="text/javascript"
+ src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
+</script>
+ </head>
+
+ <body>
+ <div class="navbar">
+ <div class="navbar-inner">
+ <div class="container">
+ <a class="brand" href="#">mkin 0.9.43</a>
+ <div class="nav">
+ <ul class="nav">
+ <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li>
+ </ul>
+ </div>
+ </div>
+ </div>
+</div>
+
+ <div class="container">
+ <header>
+
+ </header>
+
+ <h1>
+ Add normally distributed errors to simulated kinetic degradation data
+</h1>
+
+<div class="row">
+ <div class="span8">
+ <h2>Usage</h2>
+ <pre><div>add_err(prediction, sdfunc, n&nbsp;=&nbsp;1000, LOD&nbsp;=&nbsp;0.1, reps&nbsp;=&nbsp;2, digits&nbsp;=&nbsp;1, seed&nbsp;=&nbsp;NA)</div></pre>
+
+ <h2>Arguments</h2>
+ <dl>
+ <dt>prediction</dt>
+ <dd>
+ A prediction from a kinetic model as produced by <code><a href='mkinpredict.html'>mkinpredict</a></code>.
+ </dd>
+ <dt>sdfunc</dt>
+ <dd>
+ A function taking the predicted value as its only argument and returning
+ a standard deviation that should be used for generating the random error
+ terms for this value.
+ </dd>
+ <dt>n</dt>
+ <dd>
+ The number of datasets to be generated.
+ </dd>
+ <dt>LOD</dt>
+ <dd>
+ The limit of detection (LOD). Values that are below the LOD after adding
+ the random error will be set to NA.
+ </dd>
+ <dt>reps</dt>
+ <dd>
+ The number of replicates to be generated within the datasets.
+ </dd>
+ <dt>digits</dt>
+ <dd>
+ The number of digits to which the values will be rounded.
+ </dd>
+ <dt>seed</dt>
+ <dd>
+ The seed used for the generation of random numbers. If NA, the seed
+ is not set.
+ </dd>
+ </dl>
+
+ <div class="Description">
+ <h2>Description</h2>
+
+ <p>Normally distributed errors are added to data predicted for a specific
+ degradation model using <code><a href='mkinpredict.html'>mkinpredict</a></code>. The variance of the error
+ may depend on the predicted value and is specified as a standard deviation.</p>
+
+ </div>
+
+ <div class="Value">
+ <h2>Value</h2>
+
+ <p><dl>
+ A list of datasets compatible with <code><a href='mmkin.html'>mmkin</a></code>, i.e.
+ the components of the list are datasets compatible with
+ <code><a href='mkinfit.html'>mkinfit</a></code>.
+</dl></p>
+
+ </div>
+
+ <div class="References">
+ <h2>References</h2>
+
+ <p>Ranke J and Lehmann R (2015) To t-test or not to t-test, that is the question. XV Symposium on Pesticide Chemistry 2-4 September 2015, Piacenza, Italy
+ http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</p>
+
+ </div>
+
+ <h2 id="examples">Examples</h2>
+ <pre class="examples"><div class='input'># The kinetic model
+m_SFO_SFO &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;M1&quot;),
+ M1 = mkinsub(&quot;SFO&quot;), use_of_ff = &quot;max&quot;)
+</div>
+<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong>
+<div class='input'>
+# Generate a prediction for a specific set of parameters
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+# This is the prediction used for the &quot;Type 2 datasets&quot; on the Piacenza poster
+# from 2015
+d_SFO_SFO &lt;- mkinpredict(m_SFO_SFO,
+ c(k_parent = 0.1, f_parent_to_M1 = 0.5,
+ k_M1 = log(2)/1000),
+ c(parent = 100, M1 = 0),
+ sampling_times)
+
+# Add an error term with a constant (independent of the value) standard deviation
+# of 10, and generate three datasets
+d_SFO_SFO_err &lt;- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )
+
+# Name the datasets for nicer plotting
+names(d_SFO_SFO_err) &lt;- paste(&quot;Dataset&quot;, 1:3)
+
+# Name the model in the list of models (with only one member in this case)
+# for nicer plotting later on.
+# Be quiet and use the faster Levenberg-Marquardt algorithm, as the datasets
+# are easy and examples are run often. Use only one core not to offend CRAN
+# checks
+f_SFO_SFO &lt;- mmkin(list(&quot;SFO-SFO&quot; = m_SFO_SFO),
+ d_SFO_SFO_err, cores = 1,
+ quiet = TRUE, method.modFit = &quot;Marq&quot;)
+
+plot(f_SFO_SFO)
+</div>
+<p><img src='add_err-4.png' alt='' width='540' height='400' /></p>
+<div class='input'>
+# We would like to inspect the fit for dataset 3 more closely
+# Using double brackets makes the returned object an mkinfit object
+# instead of a list of mkinfit objects, so plot.mkinfit is used
+plot(f_SFO_SFO[[3]], show_residuals = TRUE)
+</div>
+<p><img src='add_err-6.png' alt='' width='540' height='400' /></p>
+<div class='input'>
+# If we use single brackets, we should give two indices (model and dataset),
+# and plot.mmkin is used
+plot(f_SFO_SFO[1, 3])
+</div>
+<p><img src='add_err-8.png' alt='' width='540' height='400' /></p>
+<div class='input'></div></pre>
+ </div>
+ <div class="span4">
+ <!-- <ul>
+ <li>add_err</li>
+ </ul>
+ <ul>
+ <li> manip </li>
+ </ul> -->
+
+
+ <h2>Author</h2>
+
+ Johannes Ranke
+
+
+ </div>
+</div>
+
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