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diff --git a/inst/web/add_err.html b/inst/web/add_err.html deleted file mode 100644 index c1f2f42f..00000000 --- a/inst/web/add_err.html +++ /dev/null @@ -1,200 +0,0 @@ -<!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<title>add_err. mkin 0.9.44.9000</title> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> -<meta name="author" content=" - Johannes Ranke -"> - -<link href="css/bootstrap.css" rel="stylesheet"> -<link href="css/bootstrap-responsive.css" rel="stylesheet"> -<link href="css/highlight.css" rel="stylesheet"> -<link href="css/staticdocs.css" rel="stylesheet"> - -<!--[if lt IE 9]> - <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> -<![endif]--> - -<script type="text/x-mathjax-config"> - MathJax.Hub.Config({ - tex2jax: { - inlineMath: [ ['$','$'], ["\\(","\\)"] ], - processEscapes: true - } - }); -</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 0.9.44.9000</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 = 1000, LOD = 0.1, reps = 2, digits = 1, seed = 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 <- mkinmod(parent = mkinsub("SFO", "M1"), - M1 = mkinsub("SFO"), use_of_ff = "max") -</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 "Type 2 datasets" on the Piacenza poster -# from 2015 -d_SFO_SFO <- mkinpredict(m_SFO_SFO, - c(k_parent = 0.1, f_parent_to_M1 = 0.5, - k_M1 = log(2)/1000), - c(parent = 100, M1 = 0), - sampling_times) - -# Add an error term with a constant (independent of the value) standard deviation -# of 10, and generate three datasets -d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 ) - -# Name the datasets for nicer plotting -names(d_SFO_SFO_err) <- paste("Dataset", 1:3) - -# Name the model in the list of models (with only one member in this case) -# for nicer plotting later on. -# Be quiet and use 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 <- mmkin(list("SFO-SFO" = m_SFO_SFO), - d_SFO_SFO_err, cores = 1, - quiet = TRUE, method.modFit = "Marq") - -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> - - <footer> - <p class="pull-right"><a href="#">Back to top</a></p> -<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> - </footer> - </div> - </body> -</html>
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