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<title>mkinerrmin. mkin 0.9.42</title>
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<h1>
Calculate the minimum error to assume in order to pass the variance test
</h1>
<div class="row">
<div class="span8">
<h2>Usage</h2>
<pre><div>mkinerrmin(fit, alpha = 0.05)</div></pre>
<h2>Arguments</h2>
<dl>
<dt>fit</dt>
<dd>
an object of class <code><a href='mkinfit.html'>mkinfit</a></code>.
</dd>
<dt>alpha</dt>
<dd>
The confidence level chosen for the chi-squared test.
</dd>
</dl>
<div class="Description">
<h2>Description</h2>
<p>This function uses <code><a href='http://www.inside-r.org/r-doc/stats/optimize'>optimize</a></code> in order to iteratively find the
smallest relative error still resulting in passing the chi-squared test
as defined in the FOCUS kinetics report from 2006.</p>
</div>
<div class="Value">
<h2>Value</h2>
<p><dl>
A dataframe with the following components:
<dt>err.min</dt><dd>The relative error, expressed as a fraction.</dd></p>
<p><dt>n.optim</dt><dd>The number of optimised parameters attributed to the data series.</dd></p>
<p><dt>df</dt><dd>The number of remaining degrees of freedom for the chi2 error level
calculations. Note that mean values are used for the chi2 statistic and
therefore every time point with observed values in the series only counts
one time.</dd></p>
<p>The dataframe has one row for the total dataset and one further row for
each observed state variable in the model.
</dl></p>
</div>
<div class="Details">
<h2>Details</h2>
<p>This function is used internally by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>.</p>
</div>
<div class="References">
<h2>References</h2>
<p>FOCUS (2006) “Guidance Document on Estimating Persistence and
Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
Registration” Report of the FOCUS Work Group on Degradation Kinetics,
EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
<a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p>
</div>
<h2 id="examples">Examples</h2>
<pre class="examples"><div class='input'>SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
m1 = list(type = "SFO"),
use_of_ff = "max")
</div>
<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong>
<div class='input'>
fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
round(mkinerrmin(fit_FOCUS_D), 4)
</div>
<div class='output'> err.min n.optim df
All data 0.0640 4 15
parent 0.0646 2 7
m1 0.0469 2 8
</div>
<div class='input'>fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)
round(mkinerrmin(fit_FOCUS_E), 4)
</div>
<div class='output'> err.min n.optim df
All data 0.1544 4 13
parent 0.1659 2 7
m1 0.1095 2 6
</div></pre>
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