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+<meta property="og:title" content="Confidence intervals for parameters of mkinfit objects — confint.mkinfit" />
+<meta property="og:description" content="The default method 'quadratic' is based on the quadratic approximation of
+the curvature of the likelihood function at the maximum likelihood parameter
+estimates.
+The alternative method 'profile' is based on the profile likelihood for each
+parameter. The 'profile' method uses two nested optimisations and can take a
+very long time, even if parallelized by specifying 'cores' on unixoid
+platforms. The speed of the method could likely be improved by using the
+method of Venzon and Moolgavkar (1988)." />
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+ <h1>Confidence intervals for parameters of mkinfit objects</h1>
+ <small class="dont-index">Source: <a href='http://github.com/jranke/mkin/blob/master/R/confint.mkinfit.R'><code>R/confint.mkinfit.R</code></a></small>
+ <div class="hidden name"><code>confint.mkinfit.Rd</code></div>
+ </div>
+
+ <div class="ref-description">
+ <p>The default method 'quadratic' is based on the quadratic approximation of
+the curvature of the likelihood function at the maximum likelihood parameter
+estimates.
+The alternative method 'profile' is based on the profile likelihood for each
+parameter. The 'profile' method uses two nested optimisations and can take a
+very long time, even if parallelized by specifying 'cores' on unixoid
+platforms. 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'>"quadratic"</span>, <span class='st'>"profile"</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'>cores</span> <span class='kw'>=</span> <span class='kw pkg'>parallel</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span>(),
+ <span class='kw'>rel_tol</span> <span class='kw'>=</span> <span class='fl'>0.01</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 '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.
+The 'profile' method searches the parameter space for the
+cutoff of the confidence intervals by means of a likelihood ratio test.</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>cores</th>
+ <td><p>The number of cores to be used for multicore processing.
+On Windows machines, cores &gt; 1 is currently not supported.</p></td>
+ </tr>
+ <tr>
+ <th>rel_tol</th>
+ <td><p>If the method is 'profile', what should be the accuracy
+of the lower and upper bounds, relative to the estimate obtained from
+the quadratic method?</p></td>
+ </tr>
+ <tr>
+ <th>quiet</th>
+ <td><p>Should we suppress the message "Profiling the likelihood"</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>Bates DM and Watts GW (1988) Nonlinear regression analysis &amp; its applications</p>
+<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'>&lt;-</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'>#&gt; 2.5% 97.5%
+#&gt; parent_0 71.8242430 93.1600766
+#&gt; k_parent_sink 0.2109541 0.4440528
+#&gt; 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'>#&gt; <span class='message'>Profiling the likelihood</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 73.0641834 92.1392181
+#&gt; k_parent_sink 0.2170293 0.4235348
+#&gt; sigma 3.1307772 8.0628314</div><div class='input'>
+<span class='co'># Set the number of cores for the profiling method for further examples</span>
+<span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/identical.html'>identical</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/Sys.getenv.html'>Sys.getenv</a></span>(<span class='st'>"NOT_CRAN"</span>), <span class='st'>"true"</span>)) {
+ <span class='no'>n_cores</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>parallel</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span>() - <span class='fl'>1</span>
+} <span class='kw'>else</span> {
+ <span class='no'>n_cores</span> <span class='kw'>&lt;-</span> <span class='fl'>1</span>
+}
+<span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/Sys.getenv.html'>Sys.getenv</a></span>(<span class='st'>"TRAVIS"</span>) <span class='kw'>!=</span> <span class='st'>""</span>) <span class='no'>n_cores</span> <span class='kw'>=</span> <span class='fl'>1</span>
+<span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>"sysname"</span>] <span class='kw'>==</span> <span class='st'>"Windows"</span>) <span class='no'>n_cores</span> <span class='kw'>=</span> <span class='fl'>1</span>
+
+<span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>SFO_SFO.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>),
+ <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>f_d_1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#&gt; user system elapsed
+#&gt; 3.707 1.077 3.444 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span>
+<span class='co'># If we additionally exclude parent_0 (the confidence of which is often of</span>
+<span class='co'># minor interest), we get a nice performance improvement from about 50</span>
+<span class='co'># seconds to about 12 seconds if we use at least four cores</span>
+<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile_no_parent_0</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>,
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"k_parent_sink"</span>, <span class='st'>"k_parent_m1"</span>, <span class='st'>"k_m1_sink"</span>, <span class='st'>"sigma"</span>), <span class='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>))</div><div class='output co'>#&gt; <span class='message'>Profiling the likelihood</span></div><div class='output co'>#&gt; <span class='warning'>Warning: scheduled cores 2, 1, 3 encountered errors in user code, all values of the jobs will be affected</span></div><div class='output co'>#&gt; <span class='error'>Error in dimnames(x) &lt;- dn: length of 'dimnames' [2] not equal to array extent</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.011 0.026 0.207</span></div><div class='input'><span class='no'>ci_profile</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 96.456003640 1.027703e+02
+#&gt; k_parent 0.090911032 1.071578e-01
+#&gt; k_m1 0.003892605 6.702778e-03
+#&gt; f_parent_to_m1 0.471328495 5.611550e-01
+#&gt; sigma 2.535612399 3.985263e+00</div><div class='input'><span class='no'>ci_quadratic_transformed</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)
+<span class='no'>ci_quadratic_transformed</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 96.403839476 1.027931e+02
+#&gt; k_parent 0.090823790 1.072543e-01
+#&gt; k_m1 0.004012216 6.897547e-03
+#&gt; f_parent_to_m1 0.469118713 5.595960e-01
+#&gt; sigma 2.396089689 3.854918e+00</div><div class='input'><span class='no'>ci_quadratic_untransformed</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
+<span class='no'>ci_quadratic_untransformed</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 96.403839429 1.027931e+02
+#&gt; k_parent 0.090491931 1.069035e-01
+#&gt; k_m1 0.003835483 6.685819e-03
+#&gt; f_parent_to_m1 0.469113364 5.598386e-01
+#&gt; sigma 2.396089689 3.854918e+00</div><div class='input'><span class='co'># Against the expectation based on Bates and Watts (1988), the confidence</span>
+<span class='co'># intervals based on the internal parameter transformation are less</span>
+<span class='co'># congruent with the likelihood based intervals. Note the superiority of the</span>
+<span class='co'># interval based on the untransformed fit for k_m1_sink</span>
+<span class='no'>rel_diffs_transformed</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span>((<span class='no'>ci_quadratic_transformed</span> - <span class='no'>ci_profile</span>)/<span class='no'>ci_profile</span>)
+<span class='no'>rel_diffs_untransformed</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span>((<span class='no'>ci_quadratic_untransformed</span> - <span class='no'>ci_profile</span>)/<span class='no'>ci_profile</span>)
+<span class='no'>rel_diffs_transformed</span> <span class='kw'>&lt;</span> <span class='no'>rel_diffs_untransformed</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 TRUE TRUE
+#&gt; k_parent TRUE TRUE
+#&gt; k_m1 FALSE FALSE
+#&gt; f_parent_to_m1 TRUE FALSE
+#&gt; sigma FALSE TRUE</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>signif</a></span>(<span class='no'>rel_diffs_transformed</span>, <span class='fl'>3</span>)</div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 0.000541 0.000222
+#&gt; k_parent 0.000960 0.000900
+#&gt; k_m1 0.030700 0.029100
+#&gt; f_parent_to_m1 0.004690 0.002780
+#&gt; sigma 0.055000 0.032700</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>signif</a></span>(<span class='no'>rel_diffs_untransformed</span>, <span class='fl'>3</span>)</div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 0.000541 0.000222
+#&gt; k_parent 0.004610 0.002370
+#&gt; k_m1 0.014700 0.002530
+#&gt; f_parent_to_m1 0.004700 0.002350
+#&gt; sigma 0.055000 0.032700</div><div class='input'>
+
+<span class='co'># Investigate a case with formation fractions</span>
+<span class='no'>f_d_2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>ci_profile_ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>)</div><div class='output co'>#&gt; <span class='message'>Profiling the likelihood</span></div><div class='input'><span class='no'>ci_profile_ff</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 96.456003640 1.027703e+02
+#&gt; k_parent 0.090911032 1.071578e-01
+#&gt; k_m1 0.003892605 6.702778e-03
+#&gt; f_parent_to_m1 0.471328495 5.611550e-01
+#&gt; sigma 2.535612399 3.985263e+00</div><div class='input'><span class='no'>ci_quadratic_transformed_ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)
+<span class='no'>ci_quadratic_transformed_ff</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 96.403839476 1.027931e+02
+#&gt; k_parent 0.090823790 1.072543e-01
+#&gt; k_m1 0.004012216 6.897547e-03
+#&gt; f_parent_to_m1 0.469118713 5.595960e-01
+#&gt; sigma 2.396089689 3.854918e+00</div><div class='input'><span class='no'>ci_quadratic_untransformed_ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
+<span class='no'>ci_quadratic_untransformed_ff</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 96.403839429 1.027931e+02
+#&gt; k_parent 0.090491931 1.069035e-01
+#&gt; k_m1 0.003835483 6.685819e-03
+#&gt; f_parent_to_m1 0.469113364 5.598386e-01
+#&gt; sigma 2.396089689 3.854918e+00</div><div class='input'><span class='no'>rel_diffs_transformed_ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span>((<span class='no'>ci_quadratic_transformed_ff</span> - <span class='no'>ci_profile_ff</span>)/<span class='no'>ci_profile_ff</span>)
+<span class='no'>rel_diffs_untransformed_ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span>((<span class='no'>ci_quadratic_untransformed_ff</span> - <span class='no'>ci_profile_ff</span>)/<span class='no'>ci_profile_ff</span>)
+<span class='co'># While the confidence interval for the parent rate constant is closer to</span>
+<span class='co'># the profile based interval when using the internal parameter</span>
+<span class='co'># transformation, the interval for the metabolite rate constant is 'better</span>
+<span class='co'># without internal parameter transformation.</span>
+<span class='no'>rel_diffs_transformed_ff</span> <span class='kw'>&lt;</span> <span class='no'>rel_diffs_untransformed_ff</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 TRUE TRUE
+#&gt; k_parent TRUE TRUE
+#&gt; k_m1 FALSE FALSE
+#&gt; f_parent_to_m1 TRUE FALSE
+#&gt; sigma FALSE TRUE</div><div class='input'><span class='no'>rel_diffs_transformed_ff</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 0.0005408078 0.0002217796
+#&gt; k_parent 0.0009596417 0.0009003876
+#&gt; k_m1 0.0307277372 0.0290579184
+#&gt; f_parent_to_m1 0.0046884131 0.0027782558
+#&gt; sigma 0.0550252516 0.0327066836</div><div class='input'><span class='no'>rel_diffs_untransformed_ff</span></div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 0.0005408083 0.000221780
+#&gt; k_parent 0.0046100096 0.002373023
+#&gt; k_m1 0.0146746467 0.002530101
+#&gt; f_parent_to_m1 0.0046997600 0.002346022
+#&gt; sigma 0.0550252516 0.032706684</div><div class='input'>
+<span class='co'># The profiling for the following fit does not finish in a reasonable time,</span>
+<span class='co'># therefore we use the quadratic approximation</span>
+<span class='no'>m_synth_DFOP_par</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"DFOP"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)),
+ <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>),
+ <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>),
+ <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>DFOP_par_c</span> <span class='kw'>&lt;-</span> <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>12</span>]]$<span class='no'>data</span>
+<span class='no'>f_tc_2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_DFOP_par</span>, <span class='no'>DFOP_par_c</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>,
+ <span class='kw'>error_model_algorithm</span> <span class='kw'>=</span> <span class='st'>"direct"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Optimisation did not converge:</span>
+#&gt; <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_tc_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 95.654015524 105.79279749
+#&gt; k_M1 0.037723773 0.04447598
+#&gt; k_M2 0.008586438 0.01078076
+#&gt; f_parent_to_M1 0.230403596 0.61953014
+#&gt; f_parent_to_M2 0.162909765 0.38019017
+#&gt; k1 0.275434628 0.33331386
+#&gt; k2 0.018602188 0.02249211
+#&gt; g 0.675149759 0.73520889
+#&gt; sigma_low 0.251416929 0.84272023
+#&gt; rsd_high 0.040371818 0.07666540</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_tc_2</span>, <span class='st'>"parent_0"</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#&gt; 2.5% 97.5%
+#&gt; parent_0 95.65402 105.7928</div><div class='input'># }
+</div></pre>
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