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<h1>Confidence intervals for parameters of mkinfit objects</h1>
<div class="hidden name"><code>confint.mkinfit.Rd</code></div>
</div>
<div class="ref-description">
<p>The default method 'profile' is based on the profile likelihood for each
parameter. The method uses two nested optimisations. The speed of the method
could likely be improved by using the method of Venzon and Moolgavkar (1988).</p>
</div>
<pre class="usage"><span class='co'># S3 method for mkinfit</span>
<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>object</span>, <span class='no'>parm</span>, <span class='kw'>level</span> <span class='kw'>=</span> <span class='fl'>0.95</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span> -
<span class='no'>level</span>, <span class='no'>cutoff</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"profile"</span>, <span class='st'>"quadratic"</span>),
<span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>backtransform</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>distribution</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"student_t"</span>, <span class='st'>"normal"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='no'>...</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>object</th>
<td><p>An <code><a href='mkinfit.html'>mkinfit</a></code> object</p></td>
</tr>
<tr>
<th>parm</th>
<td><p>A vector of names of the parameters which are to be given
confidence intervals. If missing, all parameters are considered.</p></td>
</tr>
<tr>
<th>level</th>
<td><p>The confidence level required</p></td>
</tr>
<tr>
<th>alpha</th>
<td><p>The allowed error probability, overrides 'level' if specified.</p></td>
</tr>
<tr>
<th>cutoff</th>
<td><p>Possibility to specify an alternative cutoff for the difference
in the log-likelihoods at the confidence boundary. Specifying an explicit
cutoff value overrides arguments 'level' and 'alpha'</p></td>
</tr>
<tr>
<th>method</th>
<td><p>The 'profile' method searches the parameter space for the
cutoff of the confidence intervals by means of a likelihood ratio test.
The 'quadratic' method approximates the likelihood function at the
optimised parameters using the second term of the Taylor expansion, using
a second derivative (hessian) contained in the object.</p></td>
</tr>
<tr>
<th>transformed</th>
<td><p>If the quadratic approximation is used, should it be
applied to the likelihood based on the transformed parameters?</p></td>
</tr>
<tr>
<th>backtransform</th>
<td><p>If we approximate the likelihood in terms of the
transformed parameters, should we backtransform the parameters with
their confidence intervals?</p></td>
</tr>
<tr>
<th>distribution</th>
<td><p>For the quadratic approximation, should we use
the student t distribution or assume normal distribution for
the parameter estimate</p></td>
</tr>
<tr>
<th>quiet</th>
<td><p>Should we suppress messages?</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Not used</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>A matrix with columns giving lower and upper confidence limits for
each parameter.</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>Pawitan Y (2013) In all likelihood - Statistical modelling and
inference using likelihood. Clarendon Press, Oxford.</p>
<p>Venzon DJ and Moolgavkar SH (1988) A Method for Computing
Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
87–94.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"SFO"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5%
#> parent_0 71.8242430 93.1600766
#> k_parent_sink 0.2109541 0.4440528
#> sigma 1.9778868 7.3681380</div><div class='input'><span class='co'># \dontrun{</span>
<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>)</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> 2.5% 97.5%
#> parent_0 73.0641834 92.1392181
#> k_parent_sink 0.2170293 0.4235348
#> sigma 3.1307772 8.0628314</div><div class='input'># }
</div></pre>
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<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#arguments">Arguments</a></li>
<li><a href="#value">Value</a></li>
<li><a href="#references">References</a></li>
<li><a href="#examples">Examples</a></li>
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