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<h1>Method to get the names of ill-defined parameters</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/illparms.R" class="external-link"><code>R/illparms.R</code></a></small>
<div class="hidden name"><code>illparms.Rd</code></div>
</div>
<div class="ref-description">
<p>The method for generalised nonlinear regression fits as obtained
with <a href="mkinfit.html">mkinfit</a> and <a href="mmkin.html">mmkin</a> checks if the degradation parameters
pass the Wald test (in degradation kinetics often simply called t-test) for
significant difference from zero. For this test, the parameterisation
without parameter transformations is used.</p>
</div>
<div id="ref-usage">
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for mkinfit</span></span>
<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for mmkin</span></span>
<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for illparms.mmkin</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for saem.mmkin</span></span>
<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for mhmkin</span></span>
<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for illparms.mhmkin</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
</div>
<div id="arguments">
<h2>Arguments</h2>
<dl><dt>object</dt>
<dd><p>The object to investigate</p></dd>
<dt>...</dt>
<dd><p>For potential future extensions</p></dd>
<dt>conf.level</dt>
<dd><p>The confidence level for checking p values</p></dd>
<dt>x</dt>
<dd><p>The object to be printed</p></dd>
<dt>random</dt>
<dd><p>For hierarchical fits, should random effects be tested?</p></dd>
<dt>errmod</dt>
<dd><p>For hierarchical fits, should error model parameters be
tested?</p></dd>
</dl></div>
<div id="value">
<h2>Value</h2>
<p>For <a href="mkinfit.html">mkinfit</a> or <a href="saem.html">saem</a> objects, a character vector of parameter
names. For <a href="mmkin.html">mmkin</a> or <a href="mhmkin.html">mhmkin</a> objects, a matrix like object of class
'illparms.mmkin' or 'illparms.mhmkin'. The latter objects have a suitable
printing method.</p>
</div>
<div id="details">
<h2>Details</h2>
<p>The method for hierarchical model fits, also known as nonlinear
mixed-effects model fits as obtained with <a href="saem.html">saem</a> and <a href="mhmkin.html">mhmkin</a>
checks if any of the confidence intervals for the random
effects expressed as standard deviations include zero, and if
the confidence intervals for the error model parameters include
zero.</p>
</div>
<div id="ref-examples">
<h2>Examples</h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu">illparms</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> [1] "parent_0" "alpha" "beta" "sigma" </span>
<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
<span class="r-in"><span><span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
<span class="r-in"><span> <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu">illparms</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> dataset</span>
<span class="r-out co"><span class="r-pr">#></span> model FOCUS A FOCUS C</span>
<span class="r-out co"><span class="r-pr">#></span> SFO </span>
<span class="r-out co"><span class="r-pr">#></span> FOMC parent_0, alpha, beta, sigma </span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
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