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<h1>Calculate mean degradation parameters for an mmkin row object</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mean_degparms.R" class="external-link"><code>R/mean_degparms.R</code></a></small>
<div class="hidden name"><code>mean_degparms.Rd</code></div>
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<div class="ref-description">
<p>Calculate mean degradation parameters for an mmkin row object</p>
</div>
<div id="ref-usage">
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mean_degparms</span><span class="op">(</span></span>
<span> <span class="va">object</span>,</span>
<span> random <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
<span> default_log_parms <span class="op">=</span> <span class="cn">NA</span></span>
<span><span class="op">)</span></span></code></pre></div>
</div>
<div id="arguments">
<h2>Arguments</h2>
<dl><dt>object</dt>
<dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd>
<dt>random</dt>
<dd><p>Should a list with fixed and random effects be returned?</p></dd>
<dt>test_log_parms</dt>
<dd><p>If TRUE, log parameters are only considered in
the mean calculations if their untransformed counterparts (most likely
rate constants) pass the t-test for significant difference from zero.</p></dd>
<dt>conf.level</dt>
<dd><p>Possibility to adjust the required confidence level
for parameter that are tested if requested by 'test_log_parms'.</p></dd>
<dt>default_log_parms</dt>
<dd><p>If set to a numeric value, this is used
as a default value for the tested log parameters that failed the
t-test.</p></dd>
</dl></div>
<div id="value">
<h2>Value</h2>
<p>If random is FALSE (default), a named vector containing mean values
of the fitted degradation model parameters. If random is TRUE, a list with
fixed and random effects, in the format required by the start argument of
nlme for the case of a single grouping variable ds.</p>
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