<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <title>add_err. mkin 0.9.44</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</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>. 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