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diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index c341f104..2b970d1b 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -30,28 +30,28 @@ document.addEventListener('DOMContentLoaded', function(e) { </script> <style type="text/css"> -code{white-space: pre-wrap;} -span.smallcaps{font-variant: small-caps;} -span.underline{text-decoration: underline;} -div.column{display: inline-block; vertical-align: top; width: 50%;} -div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} -ul.task-list{list-style: none;} -.display.math{display: block; text-align: center; margin: 0.5rem auto;} -</style> + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} + .display.math{display: block; text-align: center; margin: 0.5rem auto;} + 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*/ +code span.va { color: #19177c; } /* Variable */ +code span.vs { color: #4070a0; } /* VerbatimString */ +code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */ + </style> <script> // apply pandoc div.sourceCode style to pre.sourceCode instead @@ -146,26 +147,25 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } <style type="text/css"> - +/* for pandoc --citeproc since 2.11 */ div.csl-bib-body { } div.csl-entry { -clear: both; -margin-bottom: 0em; + clear: both; } .hanging div.csl-entry { -margin-left:2em; -text-indent:-2em; + margin-left:2em; + text-indent:-2em; } div.csl-left-margin { -min-width:2em; -float:left; + min-width:2em; + float:left; } div.csl-right-inline { -margin-left:2em; -padding-left:1em; + margin-left:2em; + padding-left:1em; } div.csl-indent { -margin-left: 2em; + margin-left: 2em; } </style> @@ -364,33 +364,25 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4> +<h4 class="date">Last change 18 May 2023 (rebuilt 2025-02-16)</h4> <div id="TOC"> <ul> -<li><a href="#laboratory-data-l1" id="toc-laboratory-data-l1">Laboratory -Data L1</a></li> -<li><a href="#laboratory-data-l2" id="toc-laboratory-data-l2">Laboratory -Data L2</a> +<li><a href="#laboratory-data-l1">Laboratory Data L1</a></li> +<li><a href="#laboratory-data-l2">Laboratory Data L2</a> <ul> -<li><a href="#sfo-fit-for-l2" id="toc-sfo-fit-for-l2">SFO fit for -L2</a></li> -<li><a href="#fomc-fit-for-l2" id="toc-fomc-fit-for-l2">FOMC fit for -L2</a></li> -<li><a href="#dfop-fit-for-l2" id="toc-dfop-fit-for-l2">DFOP fit for -L2</a></li> +<li><a href="#sfo-fit-for-l2">SFO fit for L2</a></li> +<li><a href="#fomc-fit-for-l2">FOMC fit for L2</a></li> +<li><a href="#dfop-fit-for-l2">DFOP fit for L2</a></li> </ul></li> -<li><a href="#laboratory-data-l3" id="toc-laboratory-data-l3">Laboratory -Data L3</a> +<li><a href="#laboratory-data-l3">Laboratory Data L3</a> <ul> -<li><a href="#fit-multiple-models" id="toc-fit-multiple-models">Fit -multiple models</a></li> -<li><a href="#accessing-mmkin-objects" id="toc-accessing-mmkin-objects">Accessing mmkin objects</a></li> +<li><a href="#fit-multiple-models">Fit multiple models</a></li> +<li><a href="#accessing-mmkin-objects">Accessing mmkin objects</a></li> </ul></li> -<li><a href="#laboratory-data-l4" id="toc-laboratory-data-l4">Laboratory -Data L4</a></li> -<li><a href="#references" id="toc-references">References</a></li> +<li><a href="#laboratory-data-l4">Laboratory Data L4</a></li> +<li><a href="#references">References</a></li> </ul> </div> @@ -398,13 +390,13 @@ Data L4</a></li> <h1>Laboratory Data L1</h1> <p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> -<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> -<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span> -<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span> -<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span> -<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div> +<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div> <p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the @@ -414,68 +406,69 @@ like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> -<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>m.L1.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>m.L1.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:47 2024 -## Date of summary: Thu Dec 19 10:28:47 2024 +## Date of fit: Sun Feb 16 14:25:37 2025 +## Date of summary: Sun Feb 16 14:25:37 2025 ## ## Equations: -## d_parent/dt = - k_parent * parent +## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.02 s +## Fitted using 133 model solutions performed in 0.106 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.85 state -## k_parent 0.10 deparm +## value type +## parent_0 89.85 state +## k_parent_sink 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 89.850000 -Inf Inf -## log_k_parent -2.302585 -Inf Inf +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 93.88778 96.5589 -43.94389 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.470 1.28200 89.740 95.200 -## log_k_parent -2.347 0.03763 -2.428 -2.267 -## sigma 2.780 0.46330 1.792 3.767 +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.28200 89.740 95.200 +## log_k_parent_sink -2.347 0.03763 -2.428 -2.267 +## sigma 2.780 0.46330 1.792 3.767 ## ## Parameter correlation: -## parent_0 log_k_parent sigma -## parent_0 1.000e+00 6.186e-01 -1.516e-09 -## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 -## sigma -1.516e-09 -3.124e-09 1.000e+00 +## parent_0 log_k_parent_sink sigma +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent_sink 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 -## k_parent 0.09561 26.57 2.487e-14 0.08824 0.1036 -## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 +## k_parent_sink 0.09561 26.57 2.487e-14 0.08824 0.1036 +## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df ## All data 3.424 2 7 ## parent 3.424 2 7 ## +## Resulting formation fractions: +## ff +## parent_sink 1 +## ## Estimated disappearance times: ## DT50 DT90 ## parent 7.249 24.08 @@ -502,33 +495,44 @@ report.</p> ## 30 parent 4.0 5.251 -1.2513</code></pre> <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> -<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - SFO"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAHgCAMAAAB6sCJ3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB1gURxuA51QUKQoCh4CoIIq9gR1jL1hiRDTqD8YkaqKxoLFHjUaTqDFRMAUSiYqKDbtiwRoN0cSoQROJYk1iA8GCdG7+3St4nHewe7Pl9vZ7n4e9LbPzfcD73LbZGYQBgAAkdgKAtAGBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAI+Qj0PtJxklo6OKqFffOwLSrNNlVMkIutz5g/6PnpCCXQnwkIRVAfmQvaKR2aDv+1pJ6lCEXr1/vtmL+NxtPfsazYEkeeAr0Yr50Nfk5vutdbs1RxJTYU6KartuQSXT0GAl20VUvxCqV2LCO21JGVQBOi1dzDoxByGDI31AGht+hNnRGqMXJ+aEWE9hgK1AWh0HXrJ1VFFc5p69EX6PqBqQ7IuECldiwjttSRlUD7tbNJCDWgjzt/10coCeOtCDW+Qy3uQ6ilgUB5Nqg7vbAZoU+1O+sLNKDksGRI6R1Nx5Y8shQoEKHj6pkjCAViHITQYfXimy1bF5QW6BFCbYuohbyNG3/R7qwvUNSYMe2NC1R6R9OxJY+sBJq0hubPxwpUT7uyLlJkYBfkqleu9CHMFyG/GdvT9E54Dc6BVpk4hJXa0XRsySMrgTR8fZE6gdWupM6eL2RQXxZ65UoLlOyl3scp9LhuO0OBSu1oMjaHv59IyFGgCwj1067sh9Cv1NGmo145g8v47PgxzW3o3VZrt5ch0L6JFN9oF/R3NBmbh99TYGQlkPY8JOPlYaQeQg+xM/LULBXk5akMBaLJ//ld6tKpWLNQhkDzaEf66m3U7Wg6tuSRo0A4QPdPP4lQK4w7IKS5Rm+FbF7gxQjF0guxCC3EV5cu1Zw8U8ebfzU7MxOo9I6mY0seWQp0GKGGN6jP6w0ROojxeoQ6ZFGL+xWoE8Y7EBpIFxqI0Hb8N0Jd6IspVVtkX6TZmdk5UOkdTceWPLIUCIchVG3ExyMcEQrD6n8yqjt1xf8qogrUv/SJN+XT/HnU11KtLPpLCXX4ekdMd4SGafelBApTX1GteUEvmjqJLrWj6diSR54CPeulPant9YxevNNZs2TzJb10ykWz5HqKWrjtoS3Z9Il236UljyXUxzRTApXasYzYUkeeAmFV/Mhm9s1Gxmvv7xSvfqOuY+t3r2uWsmZ1cHHpMCtLvZD9RVCtKr491hbqdmUoUKkdy4otceQjEMALIBBABAgEEAECAUSAQAARIBBABAgEEAECAUSAQBQ3Bo2KLr8UYAwQiGLGBdxZ7BykCghEkaP6Z3A5RU6+wWRVCYXppRYzb7FPSiKAQDTbBt0vpwQTgWbW8vhQNxuG8ff+7mE5t9v7R2I8IZWLLC0SEIhi/zSDB5sF2p+XSwwEOhyYk+V/SLPJNQyfr3v/ab+F7x0qav307wkcJ2xByFygafXP4e0N3+4fqmmbE+XjN634zMjh8+gfvMTXZ4Z6CattUW/sswvjwFPqWUOBfj6I8ZCd9FxWm6gwvPMLjGPDZn51q3HOqP+E/r2EQ+YC4R19cuqUNAw82eJR9vAlZ6qnYvrnkP/TvKA4eo7e9IZm49pwfMP3hHpWLVBwHTUpmsr6DcijP0fs3aIW8nGbXU/f63nktznC/16CIXeBVPXfGliyML9u165N+p/pgjH9M/MjjL8ZTc9hWiDNxifuBZ8t0sy+elS7t70FLePGsVgt0Ea/jerVoVl7+0zP4/93EQW5C4SXVb7+cv5zjHOfnhlACUT9zKAOXdHh9BymBdJsxIMOt7ypmVUL1NtLDd2vx85kjL98n5p5w6Oum32/4vDXNWfmh5dntL4z/0vhfzVBkL1Aw11efjdcbPgor1+iTqCDjZ7nd15bIpBmI97ctZu2nOE30OpuT5/2iFSl0k0QqW+gnT00Z+aq13Pv9FBtmyvULyQwchfojMfAjS+Xout5TcI6gfBiP98Pi0sE0mzE2XbrtOUMBSqeUqfWB4V56DZWCzS1soODw1vU7BqMP27U+YFQv5HAyFyg4oC1JwIKyy8HmELmAsUGqnBYrZtipyFhZC4QQAoIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEcCzQtADAKghi2nqAY4Harz0PWAP1/xBJoF/KLwNIgOYgEEACCAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEWItCx12u+9jW8diVBLEOgJfXjB4/q2w0Mkh4WIdAt5SN82+V+7zXcxgAEwCIEih1FTaZM2TyU2xiAAFiEQFETqUm6a2ywqdJA2aSME4DrRkNbhEDHWtPTj5vN5DaGfFjTKYZ3ArYZDW0RAhW3m5WHcXwFaxhbVhTWvMt/jKEWLBB+MFTZrX6LiIGmSgNlI3uBMP73eGpRXt0z3AaRDSBQ4aFV2zJwXEfrGF9WcGQvUErToCkh7huLAxK4jSIX5C5Qbt1N1PQvj/PHffO5DSMT5C7Qnh7qjy8m4D5fcxtGJshdoMjJ6o+DfXGK+1Nu48gDuQu0UfMMIzYM49EfcRtHHshdoHS3K9T0RYvd1OW8y11uA8kCuQuEtyjnJqzyU491NOcdbgPJAtkLhG/PHTLplHrumed5jLOSrxVxG8+6AYH0iO6W/YFzB7+Gx7kNaNXwKNDF37QzkhGoqFnH8CcYH3a/xG1Ea4ZHgcJ0A31IRiAcV+k5/bFqFLcRrZlXBDrSycEzvOzLEdWrJwn5BfpLmgbGEhRoZ81J/Wq3nvdzS24jWjOGAsX47n3+3xIP00N7pKGDtRUNl1Fzz6b42fouVmFcc+t059t4XYBdkx+p1bW+6YmqD32M2yOEnqh3kY5AB30q/nDn93dqt+M2ojVjIFCuyzX647O3TO6QhqqO3T1d8THGIc4r9k5B8ZRAgYO250bZLEiMUHxLCeT8v5PLK32AHw/p+0DzhFs6At1RDI6gPvxf4zaiNUMLpFpf0nhwTh31xxLXl+0JT5feIQ0NoaYzHJ/hIdHUjP9MSqDmKpztsphaGqukBGpJaTOwkyQPYYmNvB2Tfh3lA99AjKEFKoooab38urv6Y6T9ywbN8aV3SEN0u4fL6Bw1fX5xjc10SqBZGJ9DyRkZGRvQXVyLHv93entJCrRz8M9NqgYuTIZzIMYYHMKeVE9Xr311CHsdaYhuu5dFaXSmucIz2JMW6CuMtyINKbjWF1iyAl33yCtsug+vfIvbiNaM4Un03KDr9MXIb8ZLY9030F/o50ybCQ+p/wYt0CqMj6NH2gK1VmDJCoTfHJGZVG+3ewq3Ea0ZQ4GKV7rUrRFQRgPhNDSMms61yzqC0qiTbg+tQI+qxFKr5/eQuEDZU5za2rmf4jagVfPqjUTVzcdl7ZCGbCccmFNhLr5tM/T0nnb2/TLUAuFZlZckzlBE6gn0dpPzmhtGEhII46fnTrje4zagVcP6TnQa2tXfqcFn1JXWFn/7tvvWOy/QCKRa0dSuMX1dViLQCR9HTRMtwQXKT88sY2u53bvMGs02oIwxQ6DzbGMIK9DduT4KhKr4zTZ1M7RcgZ55nmMVUdZYnUAX7LzHR26Ii5ro62zikWj5HUzFBUCLDqZYnUBdg3M0M4UjehovUb5Aqi4xbELKGtYCFd5m/fqLoAJVK3m/67ST8RIMuri7rExnE1POWF2DssAJurmFJh5IMOkjcfI4NjHljNUJlKAIjk2+mno2LqSiiXdNmQj01Ossm6AyxuoEwvu7qZ+pKLonmijAqJfWdQHFrKLKFusTCOPMK0lJKYZ3Qy8N1WG/iUEdqiD6PPqPofVaTSvzvqrssUaBaIqvF5Re8XibDtvvmFTwh/sjvNMjOu1CRO1/zYgvG6xPoP2hA9biGBdku8REhy0OPzCq58PRRZ7qZ8oLBPgTSRerE2gbatPPZoLD4oPzbGKNl2Ao0Iu6a5qoZ27UYRNfblidQK3GY/wd+pyam9PKeAmGAuGdtdsk93WvN+6yG5v4cmPNIP4HR+0ppEB2BzBOVzd6O2hvvARTgXBfG89ND9LmO3diE19u/CTA8MyBxh9+8CNQ/WUYn0XrqLnIhsZLMBbohqLNA4x/cerFJj4gGPwI9LltxCKvNp5HMva4mOiuhbFAF939nNs2rhPtzSY+IBj8CFQ4z9Pt/YIxCKH+L4yXYCzQ2TbN1p77s/CBkk18QDB4vQ90Nc5kmx7GAj2tftgzE+P4PmbEB/hHrCatjAXCs7uOGouTPH/iNj7AEZYvUNFSJxs3/8Pchge4wvIFwrgg2jeX2+gAZ0hBIIxDFnAbHeAMaQj0n9tlbsMDXCENgfAPbaGFvWUiEYFUvVdwGx/gCIkIhG8pr3GbAMANUhEIr+oCQ0FZIpIRqDiIURtGQGAkIxD+y+0OtykAXCAdgfBnvemDWEqgQ/XXTHc/CgiMhAQqaheD8fZKjZcsqGtzuvzigCBISCB81TUNO4ZTM6pu0DrIUpCSQHh595sKda8NdxTwxqGFICmBijtPr6iZQc+5TQcwF0kJhP92Uaibdm+pxG02gNlISyD8laPrFYx/th/MbTaA2UhMoOLO7pVqOFXqlMdtNoDZSEwg/E/NNdM/gt4TLQepCYTXNYHWiZaE5ATCQ2dxmQdAiPQESvc8yWUiABnSEwjv9X3GYSIAGRIUCL/3FmdpAKRIUaAXDePLLwQIgxQFwr8rb3OWCECGJAXCyzrDSxoWgjQFKu7xGVeJAGRIUyD8rzvcjbYMJCoQ3lfvCTeJAGRIVSD8wVBO8gAIkaxAeS3XcJIIQIZkBcLX3JimDvCIdAXCsU1yOEgEIEPCAuGR75HXARAiZYGeN4ojrwQgQ8oC4ctuf3FQC0CCpAXCPzQ10Q01IBTSFgiPglGgREbiAsFpkNhIXCB8RZnCTUWAeUhdILy5PjwUExP+BMpPzyxjK2cC4YkDoe87EeFJoLtzfRQIVfGbbaorKO4EKghaxlVVAHv4EeiCnff4yA1xURN9nS8ZL8GdQPi+1xHO6gLYwo9AXYO1j6kKR/Q0XoJDgfAxz7vcVQawgx+BqiXo5k47GS/BpUB4eSt4rCoW/AgUOEE3t7Cd8RKcCoSHh3NZG8ACfgRKUATHJl9NPRsXUjHBeAluBcoJWK3+3PXB6FXZXFYMlAdPV2H7uyEaRfdEEwW4FQjf9jhJadS3zeq14d5wZ1FIeLsPlHklKSnlscHKkzWctSg4vvY+Qp1ILxxKd725oSW3NQNlwuud6KxrBp2pqjJ12HP7DYTxitYvmvyunqsLw7IICE8CbeiQgh8MQMhxlYkCHB/CKMYMUT5UzwRBL+QCwo9AkajzA9zb67uDMyqZeFrOvUD5nb1O0J+Fyn+4rhowDT8C+czB+AGiR+qe3cp4Ce4Fwvdr+GViXDwnmPOaAdPwI5DLDowvI/qCOtHReAkeBMLn7VzHTG3R9SH3NQMm4UegN/rl40LHo9TcHEFuJGrZ6rniK+j/Tlj4EeiqsunyE0s8fzwxx2az8RK8CIQXBUAbaYHh6Srs2vga6juJTbaaKMCPQKrwUBiFRVh4uw9UcPOXA8mmB4bjRyCc32UeL/UCppB8k1YDHvlCK3tBsTaB8J/ucBotJFYnED4F72kIiVkC3b1MHJc/gfBmn/u81Q0YYoZAh2oihHtEkcXlUSD8SQC0CRIM9gJtqDRuA8LzFTFEcfkUCL87EHoBFgr2AjWeijOohRlNieLyKlBBH+g5SCjYC2SXqBYo0Z4oLq8C4RcdFvJZPfAS9gK1/lgt0CfNieLyKxBO9/+a1/oBHewFWmuzOBk9iq38FVFcngXCNzx38BsA0GDGVViUC0KoymyyN9L5FgifV57hOQJAY859oOxzW4+nE8blXSB8xB26ARYA67sTXcIuj795jwGwF2iADqK4AgiE19W7x38QucNeoNE0bygrTCSKK4RA+NOmhi+mAVxj7iEsu3cborh8CVT6FvS0jvBQg2fMPgc6iR6RxOVFoLQhTlXb7NNboXqvey4PcYCXmC3QuqpE1/F8CJTqvjIz/1D97/RWFY/sk8d9IOAl7AXaqGahR1eiuHwINFx9b/O6i74yRaEhhdxHAkpgL5CtGrv2V4ni8iGQt2Ys59a/6a/M6/UOdMLJI9Z0H8hT805z27Ol1mZ3fh8M4g9rEmiQus7/nAyuvLJfmwQG8QZLgVz1IIrLh0C/u28pximBnxquf9o2gvtggAaWAkVHR69y8Ir46iN/7/1EcXm5jP+1o6PS6/tXv26etJnGQzSAhv0h7P0u9FVOUV+yRn883UjMNt6zwuOWc3gJB5ghkJem28zdnkRxBXmU8ZKswA8FjScfzBBI0+lYVG2iuAILhLNaB9St3PR7eHOea9gLNLY6/bBgf3WLPISZ5EUj5Tv5ZzuNEzaqDGAv0POuqEbTGqg72WNKoQVaOSwzMEL1otYVYcNaP+bcBzq+NOKLnwjjCi3QkG34ScexxeO+K78owAZrupFYFoN2UZdoPUaMJ3yhFjCEpUBrkvAaHURxhRbok/H439/u93aELoA5hqVADsOxkw6iuEILlOFe1yOwmo/ydWgfxC1yOYT959bKvq5D644juj8TNrC1Y6ZAqQcJWxsLLdBH03DOrSJVixNTAknfSAL0YS/Q3V7T8Y6KyNXEWJYMEVqggXvUH5Mj8ceN/hU2tHXDXqA3PHfjln1u9rL813r0GRqv/ngnBuPP66UJG9uqYS9Qje/xPZSE492I4gotUHQ/+iF9Zs3r1DTG84Kwwa0Z9gI5bcE/2ubifQ5EcYUWKL9DyNl7e5tqHsrvdjssbHQrhr1AvTsebTYIZ/cPJIortEA4b1mgR/fd2oWTSlMdoAMsYS/QJXdU7QJuUHlfWaXLRXCBSnPJC/oP4gYzLuNzzlOX8NsIhwUUWSB8q+FkaNrBBfx185ufnlnGVrEFwpmdQ+GmNAfw1M3v3bk+CoSq+M02NVqG6ALhvDe7ZYmdgxXATze/F+y8x0duiIua6Ots4n6j+ALh4slNb4udg/Thp5vfrsE5mpnCET2Nl7AAgTCOqfWr2ClIHn66+a2WoJs7beKhvUUIhA8pTYyHBzCFn25+Ayfo5hYKOeQley54rxA7BYnDTze/CYrg2OSrqWfjQiomGC9hIQLhf1q+my92DpKGp25+93dTj3ip6J5oooClCISzQzrBMM8E8NbNb+aVpKQUw0ZDhTd02FuKQFj1sS+8qmE+bAV6fnjrHeqP/t+fJ/sz2Gm3QTd4p+v5aqmwnE2W/LJRuVfsFKQLS4H+8qYOTOP/qE0fnpjsdNTUFos5hNFcqDMLnmuYCUuBBrpsSNns5tVhy5FzZb1YuDNMA+oRFma8hEUJhB91GfhU7BwkCkuB3JZRk2WovDu4B+xQQHsK1Kh9e+MlLEsgnPdOC9NDlANlwFIgRLej2VH+GxqpAW3pcQYkcgijWe1+UOwUJAlbgejbOrsZvOJTMMvhO0kJhM94wYmQGfAlEMYnvYPvS0kg/Khbf3g8zxr+BMJZb7pKSiBcMLE+2btKcoStQDa2trY2SN1VdPn7bI4wOeCSJQpEnd25rxI7BanBUqB5ehDFtUyBcGqz8Bdi5yAt5PJuPFOy/9c8VewcJAUIZMj3bnFipyAlQKBX+KtZOIwyxhgQ6FWyRzdOETsHyQACGWODWxQMr8EMEMgotzr1ggF7GQECGadgthd0wMAEEMgUx2tPzhE7BwkAApnkSXij38XOwfIBgcpgk3JpUfml5A0IVBZ3unck7IXE6gGBykQV47oUWgmVBQhUDn936HFL7BwsGRCoPIqWun4DdxVNAgKVT1qXTibbNckeEIgBRV+6roTLMeOAQIy43rUdPF81CgjEDNV691l5YidhiYBATLkX4n9C7BwsEBCIOfvqDH1UfimZAQKx4HmE+1q4oi8NCMSK39t2ZtJHtowAgdihWu8xGTry0AMEYkv6O7XiTRzHHuzbLrs+PkAg9iQHvGb0Feglyt79PMbIbAAFEMgMimNqTnh1zNjvGtR3q+0eOF6EhEQEBDKLzInKyAKDdXXcj2J8ubmtvF4qA4HMJLV/g22l1yjUXWLfqMj0L2odgEBms9+/b6lLegU9HitWVfxJnHREAgQyn4Io97H3Xy7aRNDT+Mo3xMpHFEAgErJmuCx6rlvoWXP43iNTXDzkda8aBCLj1kiPb7Vn0ylur/fuNthjp7gJCQ0IRMqFXvW3ab50rg9TVu+ZLHI6QgMCkZMU0OaI2DmIBgjEBUnNO50UOweRAIE4oWhtnQHnxU5CFEAgjiiI8eopx1fpQSDOyF3lESqvu9A0IBCHvPjSI+Si2EkIDAjEKTkrPQfJ61wIBOKYnNW1+8rpaRgIxDn5sfVfS5TN8wz+BMpPzyxjqxULRF3Ux7dosalQ7CyEgSeB7s71USBUxW+2qTbCVi0QxqrELj6rZdGyjB+BLth5j4/cEBc10dfZxABKVi4QxS8hbvMeiJ0E//AjUNdgbQenhSN6Gi9h/QJhfH2889g/xU6Cb/gRqFqCbu60k/ESchAI4/RPPPocsu7zaX4ECpygm1vYzngJeQiEcd66lo2+s+aTIX4ESlAExyZfTT0bF1IxwXgJuQhEcSLE5UPrbebK01XY/m6IRtE90UQBGQmE8e2ZrgMPWmlnr7zdB8q8kpSUYvj23f0YHVW+ZVmftMmJDfBb8eq7iFYAXwIV3dS84ptb6kr2r3E6bGQ3uu25UU7hZ8ROgnv4EahwflVUdSbdL+U6E/vJ6hCm5fGX/k1XW9vQ9PwItLzShwlTK72NQaDSqI6PcAo/ZVXX9fwI1GAONdmI9oBAr5Cxson/UisazI4fgewP0NMwn1wQyAg/j3EesCNf7Cw4gh+B2kyjpw+VH4BARsle10U52ToanvEjUBSafCQP4wMVR80CgYxzc5Ffk2X/iJ0FOTxdxi+uhtKoj32eCAQyher0OJfuP0q9w0W+7gPl31D36154LNr4dhCIJm9niFPoDkl3igdNWkUmc02PGqMPSbf5IggkPvdWdXB775hEhwMCgSyC218EKt8/KkWHQCBL4cayNm5jD0nu9hAIZEHcWtGpRtiOF2KnwQoQyLK4922vaq/HSmhQIBDI4siKH+bUaelfYqfBEBDIEsk7NKF2valHpXBCBAJZKheXtHcaEmvxz+1BIAvm0fo3a7Sa85NF32UEgSybojMfBTiHxNwWOw+TgECWz8ON4e4NJux6InYeRgGBJIHq4he9HTvMO2F5I4+DQJIh99jcdo69P//Fsk6JQCBJ8WRPRIvq/ZefsxyJQCDJkbFjcvNqfT87Yxl3iUAgSZE2rm3PZbkYP949tY1jl/kHxW/PCAJJiQPK0DdHdG+i6Tvw+eF5XR1bfLDplqgpgUASotDdKyTyU//6U0vWFJz9aoinR8iKMzli5QQCSYjzVX+kpvkd3Euvvr1pUhu7tpM2XBMjJxBIQqy3VX/srPzqppwzXw6rUyN4wd77r27jFRBIQmy3UR+pvq9iYvuDvfP7ungPXnIoXbicQCAJcc0u/DnGlz0allXoxraZPZxqv/HJfmEe5INAUqJdO2W/TjXrm3jX7iWq61tn9XKp2Xf2tr/57hgNBJISt1q2HzncaxJDKe7uXTTY16Hde9+e5vE5LAgkKYp2ffwluzHJnp7+elx7h7oD58SnFPCREQgkB4rTdn4ytFHVJsM+2ZHK8WM0EEg+5F+KnzPIr2qzYQu3/sFZuxAQSG7kXoyfF9qkql//6d+fekheHQgkTwqv7Vn2bkcXp7Zhi7f8/oygIhBI1mQk/zgntIVdzc5vf7r1d7Ou1UAgAON/Tvwwa0hLR9d2I+b9ePIuq1tHIBBQwsOfNywaFeRZpUGf95dtPcfsBAkEAgzJvXrgm+mhga52jfuN/3zTmX/L/EYCgQBTPL+y7+uZwzt6Vq4TFDbnm72XMowVAoGA8si/eSru0/EDmteo2iAwaLPBWA0gEMCY2108O/ZwX1dqHQgEMKbLnEKMr9Y5pr8OBAKYctm3GOdhvC5EfyUIBDBl5+D1Das6jTjSVH8lCAQw5bh34NmirM9cSo2jDAIBTLlT4Qg1VTUI0l8JAgFM2R+gXHh0S5fA1vorQSCAKXv7347oMWz9b630V4JAAFP+c1E/r180Xn8lCAQwZuprV3H+1zVv6a8DgQDGFEV5uNr3Sy21jj+B8tMzy9gKAkmTdMNXO3gS6O5cHwVCVfxm3zRRAASyEvgR6IKd9/jIDXFRE32dLxkvAQJZCfwI1DVY219N4YiexkuAQFYCPwJVS9DNnXbSX//HUB02K9nUB1gs/AgUOEE3t7DUg5PH23TUPs6mPsBi4UegBEVwbPLV1LNxIRUTjJdo/wub+gCLhaersP3dEI2ie6KJAiCQlcDbfaDMK0lJKY9NbgaBrASx7kSDQFYCCAQQAQIBRIBAABEgEEAECAQQIZpAs2NKM2hkGAeEBnNRS1jPEVzUMqwPF7WE9RzORS1v9uailrDeyw3+b14iCfTDOANs6zXiAK9qXNTSqIovF7V4O3BRS6OqdbmopbY9F7U0su9t8H+bzHTEKY4FeoX6nAwVsnUYF7Uw/14uk70DuaiFo6P90R5c1IK7mf0MEwRiDQikDwjEGhBIHxCINSCQPiAQa0AgfUAg1oBA+oBArAGB9AGBWAMC6cO3QI1MvUHGip0juagFt/6Ti1oODuaiFhz0Gxe1nOrDRS2412lz9+RbIKN9yLKmkJuh1bhJpiir/DIMeKziohaV6dahbDA/Gb4FAqwcEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACL4FSihTfVuF4lr2anulGgMWSVJe7jISFMLYUKr2zn4f1FImoyuFuTvJasAAAP7SURBVLJknkf42Adsx+Ynw6tA+xXjt/e1v0tazQplNMVJojqK207nICNtLWQJLUbT9s+uNJ8wmZJayJIJc1yZ+DZKMj8ZXgXq1hfjHO+5pNVMIG409c83r6HpxBmV1EKUUH61ydT0w6pFRMm8rIUomSeKOIxV/qPN/8vwKVAmiqWm7/uQ1tN3HGkNiUFBttOJM9LVQpbQDUQPz5WAbhIlU1ILWTLXuqZR09eGmf+X4VOgKyiZmkYq8gnradCntX2LGMJK/KZzkZG6FrKE8tLyqOnUqrlEyZTUQv7XUSVW3WD+X4ZPgY6iq9R0A0onq6a4skvk7jFoBVkt6n89cUbqWjhIaGOlGRz8eehaiJOJtEURBH8ZPgVKQvQIMHGorNFZGJC/5QY1HVWtmKgW9b+eOCN1LcQJPQpHowuJk9HUQpzMzV0zbFaYnwyfAqWgs9Q0qgonle1EaUT7q//1xBlpDmGECR1Q+uwmT0ZbC2kyNFPrmZ8MnwI9pk/x8aR6hNU8PE+3+N6LHhDVov7XE2ekroUwoQMVJ+Ri4mR0tZAls70fvfca9MLsZHi9jO8egnGh7yzCWpLQJmr6Xm2yWjTfHaQZaQ+EJAkVeoZr50iSKamFLJlE9Cs1fbeW+cnwKlBixUVnRjqTvhlW1E65+MDkCtvJatEIRJqRuhayhI6hmetocomSKamFLJmCjr7rDk2vEG3+X4bfRxnb21bvQf4oIyeioWPHg4SVaM9eCDPS1EKUUDTS8IAomZe1kP11no3xdwikv8PMTQYepgJEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESBQeYRqXyJGS7DDGrGTsTxAoPL4JSEhwbsTNbmKhyeJnYzlAQIxoelwsTOwWEAgJmgFcqIOYbVWdrDz/fbeAKc6dJ8W6wLsmvwobm4iAwIxQV8gmwXHBihqrUxsa/scR9ksSIxQfCtydqICAjFBX6ChGKeiKRjvR5ezXRZTa8cqxU1OXEAgJugLtAzjIhRPW3TpHErOyMjYgIgHc5AwIBAT9AVaQQuUoBZoq/YCP0Xk9MQEBGKCCYGOo0ciJyY+IBATTAj0qAo9wMR84rFgpAwIxAQTAuFZlZckzlBEipydqIBATDAlkGpFU7vG0SInJy4gEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEDE/wFycCHypSvetAAAAABJRU5ErkJggg==" /><!-- --></p> +<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - SFO"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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" /><!-- --></p> <p>The residual plot can be easily obtained by</p> -<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">"Observed"</span>, <span class="at">xlab =</span> <span class="st">"Time"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">"Observed"</span>, <span class="at">xlab =</span> <span class="st">"Time"</span>)</span></code></pre></div> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>m.L1.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div> +<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>m.L1.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: ## false convergence (8)</code></pre> -<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> -<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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" /><!-- --></p> +<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious</code></pre> -<pre><code>## mkin version used for fitting: 1.2.6 +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:47 2024 -## Date of summary: Thu Dec 19 10:28:47 2024 +## Date of fit: Sun Feb 16 14:25:37 2025 +## Date of summary: Sun Feb 16 14:25:37 2025 +## +## +## Warning: Optimisation did not converge: +## false convergence (8) +## ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 342 model solutions performed in 0.045 s +## Fitted using 342 model solutions performed in 0.242 s ## ## Error model: Constant variance ## @@ -549,39 +553,29 @@ checked.</p> ## Fixed parameter values: ## None ## -## -## Warning(s): -## Optimisation did not converge: -## false convergence (8) -## -## Results: -## -## AIC BIC logLik -## 95.88782 99.44931 -43.94391 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.46 1.2820 89.71 95.21 -## log_alpha 15.08 NaN NaN NaN -## log_beta 17.43 NaN NaN NaN -## sigma 2.78 0.4569 1.80 3.76 +## Estimate Std. Error Lower Upper +## parent_0 92.47 1.2820 89.720 95.220 +## log_alpha 13.20 NaN NaN NaN +## log_beta 15.54 NaN NaN NaN +## sigma 2.78 0.4607 1.792 3.768 ## ## Parameter correlation: -## parent_0 log_alpha log_beta sigma -## parent_0 1.000000 NaN NaN -0.000772 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma -0.000772 NaN NaN 1.000000 +## parent_0 log_alpha log_beta sigma +## parent_0 1.000000 NaN NaN 0.000603 +## log_alpha NaN 1 NaN NaN +## log_beta NaN NaN 1 NaN +## sigma 0.000603 NaN NaN 1.000000 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 9.246e+01 NA NA 89.71 95.21 -## alpha 3.555e+06 NA NA NA NA -## beta 3.719e+07 NA NA NA NA -## sigma 2.780e+00 NA NA 1.80 3.76 +## Estimate t value Pr(>t) Lower Upper +## parent_0 9.247e+01 NA NA 89.720 95.220 +## alpha 5.386e+05 NA NA NA NA +## beta 5.633e+06 NA NA NA NA +## sigma 2.780e+00 NA NA 1.792 3.768 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -589,8 +583,8 @@ checked.</p> ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.25 24.09 7.25</code></pre> +## DT50 DT90 DT50back +## parent 7.249 24.08 7.249</code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters @@ -625,21 +619,23 @@ sponsored by the German Umweltbundesamt <span class="citation">(Ranke <h1>Laboratory Data L2</h1> <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> -<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> -<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span> -<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a> <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span> -<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a> <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span> -<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div> +<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> +<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span> +<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span> +<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span> +<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div> <div id="sfo-fit-for-l2" class="section level2"> <h2>SFO fit for L2</h2> <p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> -<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>m.L2.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span> -<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> -<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>m.L2.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span> +<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> +<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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/><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the @@ -659,22 +655,24 @@ kinetics.</p> <h2>FOMC fit for L2</h2> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>m.L2.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span> -<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</span></code></pre></div> -<p><img role="img" 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" /><!-- --></p> -<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>m.L2.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span> +<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</span></code></pre></div> +<p><img 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/><!-- --></p> +<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:48 2024 -## Date of summary: Thu Dec 19 10:28:48 2024 +## Date of fit: Sun Feb 16 14:25:38 2025 +## Date of summary: Sun Feb 16 14:25:38 2025 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.029 s +## Fitted using 239 model solutions performed in 0.167 s ## ## Error model: Constant variance ## @@ -695,11 +693,6 @@ checked.</p> ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 61.78966 63.72928 -26.89483 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.7700 1.6130 90.05000 97.4900 @@ -740,15 +733,19 @@ order to explain the data.</p> <div id="dfop-fit-for-l2" class="section level2"> <h2>DFOP fit for L2</h2> <p>Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level.</p> -<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>m.L2.DFOP <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"DFOP"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> -<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</span></code></pre></div> -<p><img role="img" 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" /><!-- --></p> -<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>m.L2.DFOP <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"DFOP"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> +<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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/><!-- --></p> +<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:48 2024 -## Date of summary: Thu Dec 19 10:28:48 2024 +## Date of fit: Sun Feb 16 14:25:39 2025 +## Date of summary: Sun Feb 16 14:25:39 2025 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -757,7 +754,7 @@ order to explain the data.</p> ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.08 s +## Fitted using 572 model solutions performed in 0.399 s ## ## Error model: Constant variance ## @@ -775,31 +772,26 @@ order to explain the data.</p> ## parent_0 93.950000 -Inf Inf ## log_k1 -2.302585 -Inf Inf ## log_k2 -4.605170 -Inf Inf -## g_qlogis 0.000000 -Inf Inf +## g_ilr 0.000000 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 52.36695 54.79148 -21.18347 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper -## parent_0 93.950 9.998e-01 91.5900 96.3100 -## log_k1 3.113 1.849e+03 -4369.0000 4375.0000 -## log_k2 -1.088 6.285e-02 -1.2370 -0.9394 -## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638 -## sigma 1.414 2.886e-01 0.7314 2.0960 +## parent_0 93.9500 9.998e-01 91.5900 96.3100 +## log_k1 3.1370 2.376e+03 -5615.0000 5622.0000 +## log_k2 -1.0880 6.285e-02 -1.2370 -0.9394 +## g_ilr -0.2821 7.033e-02 -0.4484 -0.1158 +## sigma 1.4140 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 6.765e-07 -9.004e-10 2.665e-01 -1.109e-09 -## log_k1 6.765e-07 1.000e+00 1.112e-04 -2.187e-04 -1.027e-05 -## log_k2 -9.004e-10 1.112e-04 1.000e+00 -7.903e-01 9.553e-09 -## g_qlogis 2.665e-01 -2.187e-04 -7.903e-01 1.000e+00 -1.545e-08 -## sigma -1.109e-09 -1.027e-05 9.553e-09 -1.545e-08 1.000e+00 +## parent_0 log_k1 log_k2 g_ilr sigma +## parent_0 1.000e+00 5.144e-07 2.339e-09 2.665e-01 -6.798e-09 +## log_k1 5.144e-07 1.000e+00 8.434e-05 -1.659e-04 -7.791e-06 +## log_k2 2.339e-09 8.434e-05 1.000e+00 -7.903e-01 -1.240e-08 +## g_ilr 2.665e-01 -1.659e-04 -7.903e-01 1.000e+00 3.195e-08 +## sigma -6.798e-09 -7.791e-06 -1.240e-08 3.195e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -807,7 +799,7 @@ order to explain the data.</p> ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100 -## k1 22.4900 5.533e-04 4.998e-01 0.0000 Inf +## k1 23.0400 4.303e-04 4.998e-01 0.0000 Inf ## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909 ## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591 ## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960 @@ -818,8 +810,8 @@ order to explain the data.</p> ## parent 2.53 4 2 ## ## Estimated disappearance times: -## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 0.5335 5.311 1.599 0.03083 2.058</code></pre> +## DT50 DT90 DT50_k1 DT50_k2 +## parent 0.5335 5.311 0.03009 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p> </div> @@ -828,21 +820,27 @@ based on the chi^2 error level criterion.</p> <h1>Laboratory Data L3</h1> <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> -<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> -<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span> -<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div> +<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> +<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span> +<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div> <div id="fit-multiple-models" class="section level2"> <h2>Fit multiple models</h2> <p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> -<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> -<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>mm.L3 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> -<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L3"</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> +<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>mm.L3 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> +<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L3"</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which @@ -857,11 +855,13 @@ as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> -<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:48 2024 -## Date of summary: Thu Dec 19 10:28:48 2024 +## Date of fit: Sun Feb 16 14:25:40 2025 +## Date of summary: Sun Feb 16 14:25:40 2025 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -870,7 +870,7 @@ summary and plot functions working on mkinfit objects.</p> ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.047 s +## Fitted using 373 model solutions performed in 0.258 s ## ## Error model: Constant variance ## @@ -888,31 +888,26 @@ summary and plot functions working on mkinfit objects.</p> ## parent_0 97.800000 -Inf Inf ## log_k1 -2.302585 -Inf Inf ## log_k2 -4.605170 -Inf Inf -## g_qlogis 0.000000 -Inf Inf +## g_ilr 0.000000 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 32.97732 33.37453 -11.48866 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 97.7500 1.01900 94.5000 101.000000 ## log_k1 -0.6612 0.10050 -0.9812 -0.341300 ## log_k2 -4.2860 0.04322 -4.4230 -4.148000 -## g_qlogis -0.1739 0.05270 -0.3416 -0.006142 +## g_ilr -0.1229 0.03727 -0.2415 -0.004343 ## sigma 1.0170 0.25430 0.2079 1.827000 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.671e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.148e-07 -## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06 -## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.929e-07 -## sigma -9.671e-08 7.148e-07 1.022e-06 -7.929e-07 1.000e+00 +## parent_0 log_k1 log_k2 g_ilr sigma +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -6.814e-07 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 3.193e-07 +## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 7.661e-07 +## g_ilr 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -8.680e-07 +## sigma -6.814e-07 3.193e-07 7.661e-07 -8.680e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -931,8 +926,8 @@ summary and plot functions working on mkinfit objects.</p> ## parent 2.225 4 4 ## ## Estimated disappearance times: -## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 7.464 123 37.03 1.343 50.37 +## DT50 DT90 DT50_k1 DT50_k2 +## parent 7.464 123 1.343 50.37 ## ## Data: ## time variable observed predicted residual @@ -944,8 +939,10 @@ summary and plot functions working on mkinfit objects.</p> ## 60 parent 22.0 23.26 -1.25919 ## 91 parent 15.0 15.18 -0.18181 ## 120 parent 12.0 10.19 1.81395</code></pre> -<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAAHgCAMAAAAytUAGAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAZcElEQVR4nO2dCVwV1RrAh6coIq4IKosKibhQmaBiWVGYC2BaWplh2SvLtBTNfUvDp1m2aM9c0jC1TCVzBbeCyizLsiyLEpcszdx4JIqy3O/N3HvBK97hu2fmnDlzuef/+3mZy8z3zekfc++ZmXO+kUBQKRLvBpgdIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQugg73eXQRj/1qgYugsd/B7Tz2qwUugi5Z/rgP2SS7ryu/UuP8UbIGVQKfz6C1ff5CtnBF0JLIxsmXri6MC2n6PByLjZwHMCyHUkM5Cdoy2nLtL4rs/66+c0HQvhZ/5SdML1/YHnMpL3Lb09tKOuT/OoxeWw0WNDpiL6xr/Xhi/2Tr2/lhLUeX7h44YIryD2aGh421vgOrDevKHh8BxHxqXawoaP0rAMuSyxe+yATot37ca0fbXnr0BL0WG/0X9GGPS80zy95k33y6YMDM3fVyQPm3LTL/ctcVypKyqq9tZdogOByeZV20CurV3MoBW4JzHT9yWPgwIely/tPddnwzkWKDjRZkiXisd/mbqS3i4tol7r4TQPk3bjLAgsHKEiiCbCv/17ho1gzb4vVH3aqWqxwXTq672eq+f96mHmMuU2qw4Z9Bc2ocuro8G6Awf3eSLEj+N1Y+tBYNUpZAEWRbCX22tz9iW7QK6h5s5Qd5sXTQvbaPetvC+j0Arw6V325/+WyH36e+Sqm9hgsa4H/1/+3+1qcvJ2SUCcpsc+HK7WnlgmwrYXXcXfbtrvsMilc+6i05xbaFN+/Kz4+Xv8Es9xb+Hm9ZO4lSe40WtLtp71VX3y26Ifg5KBMEqS3Dny8tF2RbCQW+y+3bVRQ0qoafn99jl6VjtoXSkc1DhhcDfLAU4IU2t5+i1GCDBZVGp2VFFxu7T30YLGhZjAWSQ44Yu1NdiLN5BCEIQQhCIBZ05cx5Fu0wLWSCjk8K85Kkmi0nuNPHrD6IBH3nG/rMvJUr5j8b3uB7Vg0yG0SC4npdsi0UP9yNRWPMCJGguullS5/Xp98Uc0IkKKb8QtT0zvSbYk6IBKV79Vq255ecr1bcXy0d37pqQPYttuUuScHr7gxGzTEfpP2g8z/t3HngnOrq0dFuQldXz/YpdxRj0/a5BxE/MBGEdhRjvyTKx4+bmAjCO4oeLgjvKHq4ILWO4qfhZVRzl0EJbASpdRRLDpdR+22SfBxhIwjvKPpZBZ3/H0lWLrARhHcUFUEbIhvUi8pU2cAsMBKEdhRlQe+1/BRgR7ONhIkNhpkgBFlQ2NfKwq4b6SamDT9BZ/wBfjsKpbUu0c1MGX6CztW3wNRUKPGhNXqADWwEbRxzFedbyIdY24/hxamwweQXjNgI2tlJ8mlpx/kWsqCMoA9SR7/b5FOSxMbD6BArub1r5RsoX/N74mrWuGcfUV4b455izzv2fbH6DPqvC4IA3nyGLKudagsXsyblHvu+WAn6A7mUaBO09AmyrHaqlWgKI2EHa0EYNkErBmkK9hxBax7UFOw5gj7qoynYcwRl9NIU7DmCdt2tKdhzBH2OdAZU8BxBeztqCvYcQd/frCnYcwT93FpTsOcIOhyuKdhzBP0RrCnYcwSdDtAUzE7Q/m/sCyYRlF9XUzA7Qcll0zxMIqjQR1NwRUGnngz1jd1UaYjleqdXihzf2eaFmE1Q6b80BVcQdDJ08vGLma3nqm6fK2U282o9R176Z2RLn/BUC0CTNWMaHIPl0b7tlCtjIQu6SfUeOAexkiTZ7mWaRBB4FyHbOaWCoBETlNc/Gqjeps2Vag3ZMMbrBYD7G8zdNFJ6XxYU02dd4XzvaRkpXm/Jgho8kv1y9eFwrl/PU7ZpxGYRVLtAS7Ai6OurV/9CJ1l/tE4p/807106kypX6ya9j6/wD/ZQxE5HjZEE3WaDAP1V+NyRQFtRe1tL7NvMdYtBQ/d5rJSiC1l69fNywv/VHaM/y34y69l5brqSMEPhR2iu/Xti/1HuMLGi8fKIj7Tl79uxK6TiEKLN7x8SaUFATrG6AUyocYo9ap53m+6vmypV2y695sqbdN3kF9QpSBL0GsEaycQBCXgGTCmp+TEtwBUEHA9+3wLFuz6pub/sL+ln64rz3sL8BYhVBbwB8Ip22bxCifMCbUlDEb1qCK37N77u1fniD2eoTOHMl5dLuJN+8HVKu3Ldoahd0uuYy+ddT480sKOpHLcHXdxTzjpRWsn2u5DNs68R/TYJj3g98vrFz7YSzVkEwvsbMjLFe8xwEPd5uny23WQR1+FZLMGlPOlf6KLF+q1nyN9UHkbU7bX63wTSbIMvcKN+2yvdauaCssDr51hCzCOqyR0swuSDi+7dmEXRntpZgDxLUfbuWYA8SlLRZSzCpoOJjV0h3YRZB/TTNlvKcC2bw8Ptagj1I0OA0LcEeJOipxVqCPUjQc/O1BHuQoOfVLwNWggcJmvQfLcEeJGj6C1qCPUjQrAlagqt9zXyq6gL2giqf1GsX9Opo0rQKPQyY7TzFvi9GgtBJvXZBbw4nSssBXpN67YKWDCFJywNek3rtgpY/SpKWB8ZO6r2KXdDqASRpeWDspN6r2AV9iNXT5g7fSb2wJZEkLQ94TuqV2Wn6AlXGTuo9WX73vOYC6y8+vYMwreEw7UmXHqoweCPn6bK7595vWH/xlcnnG7I7xPonpcFif8lnpsX5BvZD7LtbiNJygI2gtVLHBO9hfqmZU7yXOd/CLuhgW5K0PGAj6JZnABZKs+WliSp/InZBh1SmtJoHNoJ8twKcsY45yaztfAu7oOOhJGl5wEZQxBz581daLi/NUxlLbxd0qjFJWh6wETTbJ2VGcMegHWc3+k92voVdUJ7p61CyEVQ8JShgaNGTclcx8aLzLeyCLvqSpOUB037QLyv2qq2yCyqppiGtoXC+5GrE5WV98BZk8tov/AVpGwdsILwFhR6nm5c6vAW1/pluXurwFhTzNd281OEtKC6Lbl7q8BakbQyegfAW9NBqunmpw1vQE2YvWcpb0Mg36OalDm9Bk2fSzUsd3oJm0XxQJQt4C5r3HN281OEtaNnjdPNSh7cgjVXMjIO3oK0JdPNSh7egbLPfe+YtaF8Hunmpw1tQTiTdvNThLehPbRWEjIO3oLx6dPNSh7eg4up081KHtyDwKaSbmDbcBfmfoZuYNtwFNT9KNzFtuAtqp6k4hXFwF9T5K7qJacNd0N276CamDXdB926gm5g23AUNXEU3MW24C9I2Mdw4uAsa9SrdxLThLmjqDLqJacNd0Evj6CamDXdB/x1W6Xbc4S7oXW0PZzEM7oK2mPyqPXdBX2l7MIJhcBd0uAXdxLThXHkB4B8/0sTGwrnygoyPuUdKc668IBNi7oHAnCsvyLTXVK3UMDhXXoDSj8Ie/4IktdFwrrxwIub2m3tHDCSuA2kcnCsv9JgBI1+/kpRKktxY+FZe+LthMaROgh9aESU3FGMrL2RJ5ShPuYD97QEWDYFLtQiTGwjTnnTeb6p1wm1/Qaf8i5UCMD+ZeHI4I0EruxyAU0mSVEdtGLT9M6jbbPisa9F904mSG4omQcexm33zpNtPQffghZljq69wvkXZvPmbuo1t1LafiZ+orkHQtiaSBPGV1hgNmygfPtJn8tKEyisvQPHq4bU+cbEJXCAXtLL6UyslmOpV2d0I/w+VB6Eoj6XJqON8i6s96VJv9ec9mAByQW1HwVn5zdioSjbum3AFiuso90wnVt5RVAg45WITuEAuyDfDKihDpSiHlV8Co17Omhn0TtZEb5XpTg6C2v7kYhO4QC6owwtWQS/eVNnWvz3T0NrbabdGZQMHQd00Ff43CnJBad6pe6TTy2q8Vvn2RUe+3LpH7WrQNYKeWuhiE7ig4Vtsvr/8p1FzgkppKRdxEDRnjK5MjNHSDyrYu+YTvQPnHASlm7qWIveL9vIJWaWfZrwhF5RUhq79Ogj6x0/f0coWckGDFfoG/kv9eWeu4CAIAjQ9ps4gtB5iBd313fBzFBS7W1cqtmj+DMouf/6dJhwFPfKunkyM0SxoeS1dnxyOgqZN05OJMeSCVlmZ3jRO134dBb37iK5UbCEX5GPFN/YXXft1FPRFJ12p2GKCfhAU1K5aF8yo4CgI2qsWy+MPoaBGDuja7zWCnn74sQdfvqArHzMIBS1atOgNv+CU1yZHhm7RtV9HQedbNF2+ZnDIQV0JWUF+iA29U/nEKOn5tK79Ogp6dmC4/LrsVl0JWUEuKNh2M3lDkK79OgoKOtLwFEBpwEldGRmhQZDtVtf8Zrr26yio5uWeG+UfNx7QlZER5IKG1FPKjm2pR+8Qa/PNXDlZQb18XRkZQS7oQpzUMKqhdHeBrv06Cnqj6/7GJVeeMOdTbLT0gz55KeWVz3Tu11GQZXyTBr3D+v+jMyUbTNFRhCP3d1MZy8gdQkFLd8LSMnTt91pB8GPzyp4XzxNCQX4DoH4ZuvZbQRB01NfvZIc5DjGAd3vQzU8NjYJyMnXWga4o6EoTfZdPmEEu6Pg9Y+DDalIjfZ+qFQXBNJM+zI9cUN+gDdC+x5F7qN32sZEXYM4/IXJBDZfASWknvB+ga7/XCYJZ5nyaH7mg+h/AOz6FsFnfLJ3rBRUEm7L2Nrmg7rfuurEPFCTG6Nrv9YJgZbQZHyFBLuj7xlLd76BVDX2Vsp0IssS9pSslGzR8zV/aJ3/Fr/1N336dCIKDASacGcVmGDCOM0EwM958wxjYDAPGcSqoONp8hTzYDAPGcSoIfm70q66sDGAzDBjHuSDLo40SJ5lrMAybYcA4TgUV3nPbzf3GBpqqJhWjYcAoTgXN6Ws5E7IjO7TIyTpeMBsGjOBUUJdsgKygEx3NNImV3TBgFwsLOBKZI7/MvDNxk4ttMgJGw4AJCgs4kKCMyy/tUddM5/Wkgi5sX/O7/G1z4mB2ZQ9CJyks4MDmlkcBSlJ8zFQYj1DQz6Hy4fXMD82UCbuVbExSWMCRBYF9/x3ZPTPQRL0hQkG9/VceWB0Q3OWDHXsru3HoemGBCpxev2wfwOIo84yFIRQUoExWniMdQzZ2ubCACk/2N81JGaEgSfkY/RC9w+FyYQEVLsf+x8VmMYdUkPLfuwG/BeRiYQFVToSY5cFsjASpFBY4NL6MGsjVgK8CTTLgjJkgKxsqjMY//lIZNf+LhK644ayre2EKqSBvHx8fb8k6VNqVINXzTuQQkxkfZ4pTMkJBUxyoZOP1yTak+ORk51vggkr7POFi05jC5t78Vl8pOlZGahMb63wLXBBcuOVlkn0ygtHghZzoTkpvWM8hBvBnqEofwUhYje4oGu+3UK8g2B/Iv+A9u+Ev2aG9/tIpCDKa6Ly7pB+G44PyHmqkVxAsidA1bY8CTAdQrU5RPS13URBM6XxRw44pYpYRZmpYHk/kWxzG7IKgODH59X6J0yu7fMsU0wuCY76R6VuGN+U1T8H8gv49MmqOfGqm0uFkjvkFNTr5d+QiKG3EqQyT+QVVL4YjIe9B65/pNsBVzC+o9dcAB5u+V4/T1735Bb0ZKx9cu30qu8vEEvMLskwPvO+hoKSAnXQb4CrmFwRwcv0Hh+DzwCy6LXARdxBkI5uPIfcRBFlcDLmRINkQh6FV7iQIPgvMpNsKF3ArQfBl4410m4HjXoJgXxOV0qfMcDNB8FPwEroNwXA3QZAbZuzNILcTBH+2G2fk2Bj3EwTnugw28CqsGwqCiwmJ+upikOCOgqB4cGfD7ga5pSCwTIk4RKslCO4pCGBpU4OG47urINgWaEyX0W0FwQ/NZxjxde++guCvzgMLKbQEwY0FQeHATif0Z0FwZ0FgmR3CfACRWwsC2Bz4DpU86ri5IPil9TC2D0h0d0GQf1+XPymlcorbCwLLrKCPaeVygvsLAtjVdCa7EnFVQRCcuKMns5PXKiEIiieGZNHM50DVEASwI3gam6toVUUQnOpx21HKKa1UGUFgmV53yD7aSVkK0lBYQBevBcbXr9ON+oc1I0HaCgvoYV3bP6BwZJ1oymkZCdJYWEAP8dZqBDurPUy56jIbQVoLC+igxVHrj7t7hdEdAsJGkObCAtrp8KX1x437tjV/6n8U87IRpLewgAZefFC5ALs9vATyh4ZSHAPCRpDewgIauBR3x4pNoxpbS8l/2qo/tUdQMPoW01tYQAOlKwfdO/Vv23LhlMC3KJ2/MusHOS0sUHq+jNrUBV3LwTs6fUslEStBJUdsdxwKr5like3fwI7XHLJ8xFjSmjxLYw4VG0HFU2tJtcYptWuXq8TRP8Su4/ywJkv1H2dsBL1c/fn0UdUfB66C5P5q1xjdN6jZCGo1UX5ZJW3kLAgs74U+/Lu+FGwE1d6qvCaHFXIWBHBxeqNJuk4+2AjqOFp5/TtwOHdBAH8ObrJAR5kUNoLmSyN2XAbYWu3R8dwFAXzfPWKt5nEOjL7mU+tKufKPzUGSCQTJJ/kxMTs0hrLqB105bH00aPHHi5yvN1YQWNa1iftcU2TVueSKUJIW3v1LDXEeIwigaEnznnuUhd+27Hf9+QseJEg+7heHdcv6vUezxKi2LncgPUqQ/FeUFuH7WDHApkBX+48eJkhW0/qWG1cWwbjJLm7vcYJeGQPb45vNXXWfi9t7nKCFSm29bwf6tT3q2vYeJ+hwY+WqY3H0vY3uz3Jle48TBLPC077feFvf0oKFbW5ajE+K8TxB8NmDNyetUM7NLDv7+o/AysN7oCBHjk8NuvO9y87W7Blx3zilApuHC5J7RundA0ZeX95qXItX109vvEQIUjg6rVnMgmsv8H/cSnky99GA34UgK6XbB9R/cLPDZbXhtseqPLlACCojb3HXwOfKT/cH2OZavTBdCHLg8Iutb5j8k3Vxou1M5P6VQtC1fDumWdSL8jf/r4HKaL6Pgv8Rgipi+SIlJGr6jxuCeo+Ia/W1+BZzhmX3qOYRKbPfyFCmyQhBzvl2yo23WheEIFVsoy+EIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEKoOtVfGFFlqr+wospUf2FFlan+wgpjq7/88EAZ3q+T5OOIsdVfzq0to9knJPk4wqv6S6yW2X884FX9xdMFqVR/uYoQhCAEIQhBCEIQghCEIAQhCEEIsRMWyzzXJ1mFvklqa/olqK15sIfamgH3qK0ZGK+25pEEpYWLgzkJevsphYjANioENlRbE1RPbU2on9qaFrXU1oTXUFvTqrq1iSPy+QiyMek/amvmPq+2Jm2w2ppNvdXWfHGr2pqcSLU1ZxqprXGOEIQgBCEIQQhCEIIQhCAEIQhBCEwETVN9sPW88WprVj2ptiZTtSbQ3jvU1uS2U1uT10RtjXOYCLp4SW1NoWpdliLVrm1Jntoai/qlzbMa1jiFiaCqhBCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCAwEpXesd9d+spCt68kCi1+KqN1hHVHMTtvDyd7s7Bf5SjFBIH1BW7yeWdez9nGSkBzfZLLAKTVnbxkqbSWIKe00RvmRKo3eMqH6VIJA+oLu6glwKXQSQURRtJRMFhiUIr907O9yzB8L7pAUQVfqjpBfn69V4vrOqAs6Ly2TX4eGEYSM7xSdTBYYMEN+SUhyOSaja1cfRdBhSXkOR7p0xPWdURf0k6Q8omCe1xWXI7L9fo1NJgscGrz37GLvNJKYloqgy7lKBdxRtQpdD6QuaJekVCpeKZ1xNSCv2UJQBJEEFsVIkjSMKMYqyMqq6mMJAqkL2inlyK8rJJcfI/dQL7AKIgkcEpS2e1adJSQxZYJOD5IGFxMEUhd0QPpKfp1f09XtV/v/ZRNEEJhj/ShJ9S8liLEL2hoYtoGoldQFnfNaIb8+d4Or26dINjYQBK6QlCvvmdIhghiboK3VhhWStZL+1/zd98sduXDV2zsV+XWXTJv4XacJAvcqD8mDKT5FBDFWQcVBg0hbSV9QRrUZuwc2UJtQ5hzlECMJTPCfv22ydypJjFXQx9K45QqFrgcyONVY16lePOGphlUQQeDFia1qt3/bQhJjFbTIfjyfcj1QnKwiCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEI5hPU3357WJoJfkt5N8aMgr5MT08PvU1++QUG7OTdGDMKUogawLsF5ZhaUH35EAt5vYtv+Fsnk+o3f0/+zfJo33bvGNoU8wvynvZxklfI6xmdfC7AfO9pGSlebxnZFPMLekAZkzgSYIv0Y4F/qvzbIYFGNsX8guYAlEjvK5a+3yvtOXv27EqJaJ6DTswvaK4iKN0qaI29A3DAwKa4laBPpNOGN8WtBJ2uqcywmBpvZFPcShCMrzEzY6zXPCOb4l6CLHOjfNsuMrQp5hRkIoQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEP4P1CtmUU9s8ycAAAAASUVORK5CYII=" /><!-- --></p> +<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit @@ -962,99 +959,106 @@ parameter <code>g</code> is quite narrow.</p> <h1>Laboratory Data L4</h1> <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> -<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> -<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span> -<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div> +<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> +<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span> +<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div> <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> -<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> -<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>mm.L4 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> -<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L4"</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span> -<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a> <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAAJACAMAAAAaQUt1AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dCVwU5f/Hl7xKUTNAWEBAREExUbyv0vAovDDRMDk0LY/U0jwwNTzzSsujNMIsxCJMLH8V2M8SS9PSn5b5LzXvOwQFQa5l9/ufYw+YndnnmdmZ3YV93q8Xu7PPMzP74f3M7uzuPPOMCggWUdk7gKNDBCEgghAQQQiIIAREEAIiCAERhIAIQkAEISCCEBBBCIggBEQQAiIIARGEwNaCylUMdQGOBdRzaRxHl+31qvNI8w10GFUhwFaVK0CWbx1V/T532GVcVOf1Sx8aN05r48B2EKSmaAvpLqo6DVWqVgBrKF/1VaqXqgi6VEf1mIeLypddxiSomUpVbuPAdhDE3GsaqfoD5NRTJZXWV0UCLFS5lJsEJaq8AP5UqdhNyChohouL0wjaqWpAv1imq7x2qerRU5PG3DEJmquq+7EGLvzJ2jAIulNnZF1nEESTNFHVhn74s6reDJWfMYxB0H1Xlcqlcc+f2WKDoG6PlTuFIPo9KPUlVtAxVZ3pqgBjGOObtGZ1TzcXlQtrSC/oW1UyOIUg5j6VfYm9rvLcqXqULujd4Twl6Ar9nt0E/vjqAsD9x1UjmXn1ggIf6drVRdVZY9vA9hJU3lA1iNpt11MtfFhPNQGot2UXLTRQJQC0o/ZsMcxW9aTqFWZevSA1++q08SZkL0Gwy0VVl3qnaQmwXKV67FFmN5+gUtWvo1Klwt91VHV9H1PV+ZuZ1UXl6evr25OedJ6XGMARv7ouruPoqV3NH3FpmkRPzW/s8ojHJ9TEj4F1VXVb/cjO6cJsOa70ZO0XVOMgghAQQQiIIAREEAIiCIEUQRdHxG+TPYijIkXQ3JPQV/YgjooUQSW66yNlD+Ko8Ak61M2rwzqqys3d3X09aFe29+j9bbUZMkbcRqw2JwqnSIh7l7FnVRweQQ89v9de6J8CqmLm4YQh17RHAtOrzPDNbB1qtTiC5vmq32AmkoM9Y0tgpV/ABrjSI3gjwLSz2PkVh0fQ32rq///pfb2g371LgP7iVGmaYeKQ6NhqS1To/0yPMATt71JyPzibmjgRcLswcsm+9rduhh2enF0ZXnhumrj/AbnFGzgQTd+mJFqKxYVHkLbFyAP0V0JW0ObJTKHfRfp2dutfYXeI0cWmlkGztYdfjFlE/8GKwJZzmUfs0zKVg/cCdDnETHKTHMkCGJVJTWRS/9z22PnUYm/PmrfhcruS+JuWM3NAbvFGii7Tt1YLgrtzWjeacp9pkUCYu5wp68V+s94zuMQ/y7jusNzimBWHm54F+i87uLCsTyo9xTwtW7kjDi4GHmQm2STP+TOcZlYWObSMXVN+1707u97L6x5TOHnA98cXWI7MxeIWX2E+v/WCKP58dqz+CTdOYQoCrjB3utYJw4wzLQ7o1y90yOGnAei/eQsB3h9PTzFPy1YWeFa8vZSdNE9ya3cYKzstKA20M73axk2kH0Xf3zd4Tpnl2FUR3uKZzZnZfB+81KbnPsiJrpzZotOkxOwYgMQUmO/f/IXynCi2TggeQckvUjenW+if8IRPKXV7zFtfuab+P8YZ16wCKC08PJRKQv3NpV4j2+LoKUYQWwkj9ne8xE6yggb5MPxBvbJ+AVhP29fGDad2ipUagLeWUQ/3r80Lv7p4Pb4g4S2e3pzZLXlrtO7oRErQRz1L8gP0gq4PKNV0O5QTxdaJEHT+8YO6ijmxhtd07Ihbul9bZeorY9xMbXsqJLcs8juDoKy2ReV9dxgFsZXweb/++vm4W9Dm/oWFERt1ZzWZEfRO8a92xf8GUs2uG156NUKX8aYIQSC0xdObM7v5nvGZtV9HCRpLfQWYadiCru1a7JadE8XWiRAEh3t5tYjLNwiqXNrOo2OaoUo9LM0047ZWPjPAIAiWBwW+oTUKYiuhuOEn+vm4grSv+fu+qilTXZlV39XVNQGWeod9RRWnpwAkte17B1+O8BZPh9FvyfmfxXelBI1LprZTRtCUlCNttx4fRQli68QIEkbbecdBWx9WQCK8xdOC2M132VzN3cb3c6K39y4rDE7MCdUUqFNWTYdLvl/nRLF1QmsXJ2h7Fx3E+l6y5t9RAMEtntmcmc33Rh+vPinMm7R/+JuJZfEtfYelXItsP2xBYE4UWycEUtCZpEkJi07K85/URFCCNoYs/fjjZaHv2SSMI4ISFPiAvi1pb4MojglKUJur9O2/YTaI4pigBGX6xi5cGO+92yZhHBHkm3R+6srln9y1RRTHhOzFEEjci93+0AqSH8j+byiXR+xerGzxfJq+zV+Rju9+OZxU46MQpfKI3Ytp3l1NE+5vxX8zONuKhflJftmKhS3mkbgXiwtRKpA07CdIYC9GBCGIaxnfb9Lf0pZ1DkENpns8US+G5xdfawNJw26CCjoEsHDK4zzVP0GnvuvkDyQN+21BhSGZN2g4xXGenTprh78VKX8gadjxJbbyBF9pnGuLzsmPD+pdKnsgFFmlRYuj1nK7cjree1D9R+q41K/TzLX/AJEMvGSVoAkDy14YmTaEe+zV8QR99V+JPHnIKkFeZeBZDA9aGAv+OEEzf4z0VSojSMRRh+o8ZZ2g7n9C+CW40NrwuLh3Zxo3tfRV1i5BJwPGDFfH+3zBzaPYB9eaJghKd69N2nrdLA8RZBkiCAERhIAIQkAEISCCEDieoPGdYs6IW6RkPnPnLII2ntyiRva1rPZzUQH7k4mzCNqZkTFoQgbDV6bhNLbFBPmMK2Y7/5k6CB4b8kzg0H/HNKA7OjmQoIJ1U7cxvwooIih+9Oh2oaMZxj00Fm9rcq1yVBLb+c/UQfCYyxld1PuOtgUVtp70wbBn6MZVRNBpuB74i1nxtucBvuvLdv4zdRA81hlgxTuOJmhLPICu6w+gkKCg4GY8fYa2RQP8twfb+c/UQfDYs44oaM471M1EumuZIoKun+X7MXFb05vaMW+ynf9MHQT1gtjBbhxG0K7+lVAUSPc6sOHnoG1Rvf1jitjOf6YOgqwgbTemm6vDCNJGtY/3Zz562FJQIm9xVRxGEMDh1D+ZeyIIgQ0F5d1CLuncgjCodYL4e5gRQQYEepgRQQYE+kkTQQYE+kkTQQaEeph1CZTIo8dqlyBuD7PSV5l+j63VF6ty6vGfLmJyRYbeHeadSuwnSLdnL6RFLzN+8dKmMj1newRWn23BVLkCWeZuLE2j2FhOuf0Evdalz8g+nw7jdp7gBrrrxu1BJDWQZTTz3Delp3ukG8/6Lm7HvHAbe0pepZWCvIsrmp6FQh9OsVmLzZkhUyAUB7v+B6r2d7vGvHCj2lixSqsE+eYXP3Yccv04xWaC7jyBPxqAde9B98e+bN6Vw34vsXfUfnNC3uy4CBlo1mvyBMIgzXwsADvuxX4/DT8n7kIHuu2GvQnVrt08P5xAV1+PWj5zuiyBZMkjDuUF3fJO2pvQ2e2aHIHkyCMS5QWtpt9/eox9RY5AcuQRifKCZm6kbsZ96H5RhkBy5BGJ8oLSe5fCDe8Ly+NkCCRHHpEoL0g30XdA881Q5PWn9YHkyCMSG+zF4J//0r9/bBhhfSB58ojCFoJYyvyP4CzsvIJgRx+chZ1YUGV74bGuTDixIPg2tJK/oirOLAj6J6MXdmpBJ7yLkAs7tSCIXYxc2LkFXXMzO9OEi3MLgkXILxxOLqjY91fEwk4uCD7pgRhN2dkFabvttLywQoIqvtz4m6SFbS0IjvlYHiBIGUFFYQNnBnIPLmBhc0EwYZ7FhZURtOolgAK1lEEwbS/ojofFcU+sEVSyJJYe8TXBPM84evTNYTjfBUXlUUQQvBdhqdYaQbGR27tQJlzN8yydQenzOythnXYQpAnjHTJdjzWCmj+AvKDbJkEVGwwjYuUHjlsePlnKOu0gCI74FgpXWiOozW2AncN1RkHlbzFjqoX6Qdlv6xfxj/WPQpFOnKjPHRNnSgyE4MOW66jXWUR9szy7m7d7XORg+Fh5pHbiRAnK8+IdNQYdCMWJTOqT1p7XuXmCPE9Dfqe9klapSCdOy4Koj9Kfdhb86UyJ3bzPKOp2M/bR72oo0onTkqAvW9cJy9FFbJAUSBpxfvTlmBajf2rhQ5FOnBYE/e79qzbL6+Y/7pelBJJGXPCT839N9jqPnpMHWTtxPhzODJOk9hZeYgX9eT8uFVYPkhJIGnEht6d1GyNxyFnrBHGGxNIdZIZJGtBKeIm36Z3Ji7tA0/kTCYEoogygsplQ/Nu8dsWQwu/46gWGxLIU6Iz6x4cZ6jsApzz5T/5BCcoxYHk23DxIcATNigktjeTrh2k2JBZGoO86NupzjJ5YPFx8IJbyQ9nZXwciZ8PLgwJHUKuH/aCU25WVhjsklqhA5R3S+IoxBI3p5jbKV8TVWRQXFHC3H+QG8dRbNyTWSU++josYgjw0i05c4t/+eFFc0MawgLdDPuSbwbohsZZE8vz8iiHIryxtK4i4FIPyP7keXLzmlIh14gaq6MxzpBVD0PwBV9tOHyJ/Hl5wBKUwKBDoL49/zMpwPgddhyOrkAfYzPLs6eIz5gr+Ujh59IISExOnqBV5zW/sWXbuYvXXGYYgaQ2W4//jjRWhoq9Ij/tB8T53X44MhIOuj2dr/17/YgdikdZgk7dSN11Qh+XMwBWkUeZzR68mR3QLxmIHMiG+wRI+BeaURpHgCIqhCE0QGwiHikaZLQtue2EHMiG+wXaH3YQ9LYrxF8PIoxeUTZEj4sUrYgtyvzV9zMlqe2wMQRIbbFVT1zDxBw+RghINiA6Ew+Le3wT6bMUOxCK1wXSo3kklPL+VIwVR+4tJATMXt1koPhAGlR8M6NvkD+xAoGSDFcU0ahJh9ukB5yXWKR+gMFz2QAZ2hlRtWZQg5RpsRkKZdonZz1Q4gnxLqC8V/rIHMvJyDG4gFtEN5je43Uvo02ZbXaDe+xuXcEpxBM3otS25N/45g6IFlXbawk6cimwxsAdakOgGa5Dxf4val6Fme/J36mXWhHuZTxxBlR+NHbcdowevMZDY7z4XPZlfiO5477i2yw0tSLjBBI7T0aex9voJtdp1PY//9fwkbilSUPRliGZArb9KINFfDv/Tgj6b45N46qYDWpBggwkdp6MHSBvyDWq12vVhIQu4rzC0oANFYIOfOJOeqgD4gP7tthNCkKUGMztOd5M9Lbzh8YsZnnmgvUQ9oAyIuh+A+10M/5+VNKja0OkA59Sn4C9PhCBLDcY9Tlccwg4s8MgjdetOBviBfrBB5H0IhqB9E7VdGyvxc0cVCkKoJ9jt5dH8SczvYnyFgsfpyq5p+ebHAedNusfR7Pi7WKfqGAJJCHLO8zB1m4uzmxduMFtfzksvqI3u9c81Iq5NIS1QtvdlZCAWmzSYARxBE0b75M/pq3igTe0LUYFYbNNgenAEPdh6BpaJGMBFaqCpz2mwBNmowVisOrIqcyDNs9OwBNmqwRisOrIqd6AHYetwBNmqwRisOrIqe6AbQ3QYgmzWYDRWHVm1eSAW2zUYKg/yyKrETpySA7E4ToOhjqxK7cQpOZD+aQUbjB+FBV0+WgLafy9s4amX1onTikB6FDoUzgtS0Lsufh7fejZp8TRPvZROnFYForHQYAIoK8j7NzikSuX/rmePaz1bajABlBX0BPXXTOi7MOfLYXEvha8pCJYbTABlBblTf14W5qk2NCh7VcpIbpcz2QIBosH4UXgLysvLa0795ZlX235oUMBoMHOUFfSoAfNqs6FBFQ8EFhtMCDue1MsdGlTpQGCxwYSw51nPNh4aVBr2PS3cpkODSqNGnTePhAhCQAQhUOa0cIXy1DRBwqeFK5SnpgninhaueJ6aJoh7WrjieWqaIO5p4Q8j2TMgFfvyXNMEcU8L1/3EngEp4udZcXlqnCCKC+ZF5CVWlS7mRURQVYggBDynoBFBCIggBEQQAiIIARGEgAhCQAQhIIIQEEEIiCAERBACIggBEYTAjoLs04lTLPYTZKdOnGKxnyB7deIUif0E2aUTp3jsJ8genTglYMc3aW4nzrYKX3waB0e6nLpZZ4HrCl982jKOdzl1m3cWsIzt+0yiBNm8swAKW/eZRO7FbN1ZAEn1PpPatA9peogYrUpcHpQgwTHklQqEQdU+kyXTXqFp7atUHuReTGAMeXJs3jKOfej5q+ED3quQKU9tFJQetPv7CBEnvNY6Qahj830PABQ2xh8irtYJ4qFannZ/UTfu+Gd6OJ2gqdO1sKuDTHlqo6D7/f1CW/8hOK+4PLVREPVB4E/uQG6WcEJB4iCCEBBBCIggBEQQAicXVPjfw4hdmnML+lk9oHP729Lz1HpBQd8DvJVgcWGnFnSXHqXgXLD0PLVdUGWTXIB9z0jPU9sFQVJ46ib1ful5ar0g3RdxryAuReLcgjAgghAQQQhqlyDOVTsVz1PTBEm4aqd1eWqaIClX7bQqT00TZNVVO6XkqWmCuFftLHZVMXgplaemCRK4amfyy0rlqYn9pHk6L9hPkCP2k+Y5Nm8/Qdx+0sXqZjQNFHvNY+BQgsz6SRfdo3lPsUAY8HResJ8ggX7SygWShh3fpPmvhEIEISCCEBBBCIggBDvc29dtqKfuY/qJBvUNRXUME/Ue1U88Ws84e3iTH6Q9KV6e+oanFC5oYJhgBg/vbDGPREHlJzLVO/W0WqKfiB+on0huaKgLn62fmN7dUFTvlz8kXwkMJ0/vKez9Bg99wdOT2PtNzfQFEePZ+w+aMIOHn7CYR6IggHPG01m6H9NPbJ6unyhoaqgb/rV+4osxhqIGZVKfEi9PXCp7f9lwwsJE/XXKbnrrC6Z+wN4zx8xQEEEIiCAERBACIgiBZEF3Rhmmxv+jn9j7jn6iLMJQN9twUPOnBYaip0VcEFhKnqQD7H3ecH3BCv2FygoNZ3Ku1bfaQ7PLYvMgWZCzQAQhIIIQEEEIiCAERBACIggBEYRAPkEasx+u9RTI9hTygZ9JqqC9wYEbq5fMi+UrpT7AdjDOzlctEwKrPtldPZGnksmUHOwZW4LMJFFQvu/VwuAzVUty3GN5SgFWhgQYZuerlgmBVZcGnC4bmGVWyWQ6EXC7MHIJMpNEQanU5rIkqUrB/a6bYs1LgR6YIMAwO1+1TAisejf1Fbms1KySyZS5DmB7LDKTREErFwJ8PKlKwdh96bHmpTQ3Agyz81bLg8Cq144I9xxbbF55g/2mn991LzKTREHL6fUmmB6nvQyUIG6pMQxbwVstDwKrTlJfLBr5lnklKygtKE1oQRNSj2pQ61y2yPQ4Sh3g0SiSW2oMw1bwVsuDwKq3JtBDoZhX0pm0ccNvCy5oQqKgXJ/c0tD/VSuitiCeUjYMW8FbLQ8Cq77uc7Fo6FrzSjpTZoROeEETUnfzGWGh71YvoQTxlOo3Z7aCr1omBFb9RZDv1HLzSjrTrPqurq4JyEzkkzQCIggBEYSACEJABCEgghAQQQiIIAREEAIiCAERhIAIQkAEISCCEBBBCIggBEQQAiIIARGEgAhCQAQhIIIQSBZ0cUT8NjmDOCqSBc09CX3lDOKoSBZUors+Us4gjgpHkMrN3d19PWhXtvfo/S3AGRXduSj3kUVQviDoiT4nqs6aMcLy+JYAOVE4RULcu4w9q5JwBRUzdxOGXNMeCUyHM43oEQh3NFoE00blVab6PDTN+c1sHWrdOILYbl5g6AU2z1f9BlzpEUxNTjsr7j9RCF5Bv3vTmY/4VZ4JC74H8Pzzi665F1ElE34xzTlxSHT1a1hV6P9MjzAEsd28wNBFbH+XkvvB2ZOzK8MLz3EHmeKPj7/FGzgQTd+mJFqKVfUZOA8ZQZsnMw/8Lp7pOCsNylq/vWjvMNM8s1v/CrtDjC42tQyarT38Yswi+g9WBLacyzxin5apHLwXoMshZpKbhO3mBYYuYkeyAEZlzttwuV1J/E2Emyp5cbZ4I0WX6VvJgqgWCYS5y5kHvX480zFnNGS9snrRxqqdsPYMLvHPMq47LLc4ZsXhpmeB/ssOLizrk0pPMU/LVu6Ig4uBB5lJNslz/gynmRXkd6X8GbuI7YkcWlY4ecD3xxcADnhbPM9lbKzbgjZOYR4EXDnTsTKgfNo3qxd9yZx89Ru7zepaJ5g2qMUB/fqFDjn8NAD9N4/6P98fT08xT8tWFnhWvL2UnTRPQnfzAlMXsVu7wxj30ff3DZ6DPvUOY4tnNmdm833wUpue+yAnunJmi06TErNjABJTYL5/8xfKc6LYOlxBJ3xKqdtj3nCmI8R/F1yyetHFJwqpkv6fsnOtqf+PcYk1qwBKCw8PpZJQf3Opl9a2OHqKEcRWwoj9HS+xk6ygQT4Mfxi6eYGhp1cm1eTr6dbZvzYv/Ori9WhB6C2e3pzZLXlrtO7oRErQRz1L8gP0gq4PKNV0O5QTxdbhCoLYEbd0v7bKpAXt6RkFqxfBxBF3K5K97rFzxbiZ2vZUSG5Z5HcGQVlti8r77jAKYivh83799fOZvQcx3bx0ZzVsT6/N/QsLI6j3Wd3w0qsRuow30YLQWzy9ObOb7xmfWft1lKCx1FeAmYYt6NquxW7ZOVFsHbagyqXtPDpSmz4lqPjR7bSg4tf8PJ7Sd1U7rB6WZlpkWyufGWAQBMuDAt/QGgWxlVDc8BP9fFxBbDevMtUVtqeX9jV/31c1AOkpAElt+97BFGRxi6fD6Lfk/M/iu1KCxiUDvMUImpJypO3W46MoQWwdWhAO2s47DnYWc+UTBcHY4mlB7Oa7bK7mbuP7OdHbe5cVBifmhGoK1CmrpsMl369zotg6vmcQH2p7Fx3E+l6y6h+TC4wtntmcmc33Rh+vPinMm7R/+JuJZfEtfYelXItsP2xBYE4UW8f3DLb7Z2omRBACIggBEYSACEIgcahS50HiUKXOg9ihSvUU/tcKDsg//otyeUQPVcqS4jNAOm7fWyWDD+XykKFKyVClSJQYcJsIMiCwFyOCDHD3Yg+HMO9rLdspFUgajjPgtu4Qs2ccEAS3lAkkDYcbcDsuJFeNPHAoKZA0HG4vFhfysIXkQSZqlyB+4tybPlp/7DVpC1slKDuzLGvKJu4vvo4nyPWlVsGNPM7LHwjBG8GjhoWtj5jOKbaboIIOASyc8ji1x9nmQydMlj8QAnUBPPclFJgufnSE2WnMiJa+Suu2oMKQzBs0nOI49+7QusPawfIHQuBbAl/eggJjgz18lvnYoVZLX6WVL7GVvF0k4hq4ZrWpW8eV90CAVYEQzO2SBfBL/1ncPA53dSj/DnXrNAz/NTBH7kAo9h+hPnts5o4h7niCQiAlrOFD2MBtSqsDSc4jHYUEZT3b6wCsnid3IMl5pKOQoAf+w2f86HNc7kCS80hHqQuwnelRJ/xr9KwiA0nDIQWB1kPaR2mnEQRxH0ha2HkEfTFE0sLOI6igacHpf8T/6uE8gqCnur1/33xZA1mVRxoKCmrRF3SzRA8d6TyCHjT20MA/QbIGsiaPRJQTVNmk0wHI6SlrIGvywO2l03aKv/SUgi+xNwKHZrTeJWsga/Jc9571/tOxqHnF5bFOkGZp/eFfyRtIGmye+QsByvzOyZrHOkEA4QfFL6yYoBfSqZtB+2XNY62gNVPEL6yYoDXjAG56oLufi8ljraCrHjyn0iBQTFBJny4vqjfJm8daQdDrO9ELK7cX0x7cdVH8wsoK+uBF0Qs7z+cgmrzHH4hduHYJ4u8fVCVQ1MeyBpIGk+fqsUJJCyvRP6iKoL1PyRpIGlQe7Xiv7s3TpSwsa/8gUyADFZ4X5AyEoGRJLP3JNME8T0pEKfxf81wJ65S1f5ApkJE30CcGigiEIDZye5c0AFfzPAn02XOR4vepSvUPMk3/rRb5UcgaQc0fQF7QbZMgbeqHND0CYc4KAN2TvMeBrckjuX9QlQdP7ZExEII2twF2DtcZBZW++gpNa18467lx/3hJl+C0SpBuz15Ii15Wanhc7Kpi8Kgyz66BMgZC8GHLddTrLKI+p5husNPjByWJ/siBzIMS9FqXPiP7fDqM2/2m2hZU5inuC7RVe7ETmdTras/rlvKIxSpB3sUVTc9CoY/FQG/OlC+QNOwnyDe/+LHjkOtnMdB1N1Gbdq0S9I7ab07Imx25V7/hBHpB1HXVapUg+P00/Jxo9qsqJ9DRQDF7j9oliB9OIF3oq3/IFEgaDi5IMzjQ3R9vPBtkIDnyiER5Qdufq2y7z/9PWQLJkQeTh/+UM/fKC5qxGT6JiN2Ju7CDCFr9eJDH5/SE8oI2TISKlkFHcBd2DEEHQu7AGa/LqDyyCLrfauKWUA/sbh6OIWjBKuom/lNUHnn2YgXvTP/I/xeBec1wDEGr51A3kV+j8sgjiCYlAndhxxB0ySvl9xVBRag88gnStPkBc2HHEAQnnw+bwPwcaCNBkN4N813IQQQZsZUgXecv8BZ2VkHwYyDemBNOKwhGrMNa2HkFnXPH6lnhvIJg7nichZ1Y0ANfnO8bTiwI0sMwRqB0ZkEwGON92qkFXfJA92ByakHwTgTy87RzC6rsvhW1sBWCzM7jL0+aTxPKPS4lBtsKgr+QLzJrtiDuefwV61fThPtLX6WtBcGGPohjQFa9xATO4685LzEA7TMrLC9cu96DkH0UzbnhdVR6IGnYTxC6jyIP+wLuSQ4kDfsJQvdR5GPWMEv7+lolCKOPohlFG6YGWnobqlWCMPoociluO37LoPoWzrlBCYoygMiGmQcJjiDtiiGF/B1EOX0UdTmGUfAE+TCG7s3QTLhzMEpQjgHLs1VFcUGzYkJLI2fwzZBVWrQ4am254eHDoeyARt7Cq2SOyE0ZGyrY7R3jJVZ+KDv760DkbEYUF9TqYT8o5fazo5kwsOyFkWlDuNeyshQoo28lPGz967TnhH75wBA0ppvbKF8R/a8VFxRwtx/k8r1svMrAsxgetOAUW/ygOCo0vuUs0DwrNMIZhiAPzaITl4YjZ8PKgwRH0MawgLdDPuSp70I+CVkAAAnfSURBVP4nhF+CC61FBTry6e/U7YOOb0sIxOJXlrYVuJ8tLKD8Xuzg4jWn+OpPBowZro734R7xwgp0q+V28YFY5g+42na6iJFBFBeUwsA3Q+nutUlbr0sLdN47U3QgPdfhyCqzZxVGcUGJiYlT1PK/5k95ZvGUYggSbjCr8vCD+0HxPved2AK4gY42PyAyEItCDcYPriCNEp87fmp+QHeFM0wl5lcNkQ129jPsLm4ccATFUIQmiAqEyc9ubfy8+lc7XQhTkLgGW+Eztn2ktKue4QjKpsgptzCfWSDsWbs2ztTOjsMOxCK6wQJ98kH73Ef4S+DmoQUlGhARCFtQRaPf1DtuVRtqFUOQ6AbzeYG63abA4Lu0IGp/MSlg5uI2C0UEwhakc/v3bMvXqg2BjxIkpcH8n6wEmLYafwncPPqXWKd8gMJwEYHwX2Lz+/2U/ljfqr/jowRJarDIyORX/fPwl8DNoxfkW0J9JPQXEwh7Vs2GPv23DBpS5YQpjJeY+AarSH5lpejR1DDy6AXN6LUtufdrYgKJy6CZ3OEyXiAWBRvMHBxBlR+NHbddxClN4gO9p2Z//9Jcq8AQJNxgEo6ymNB99FS3VWbv/UhB0ZchmgHrScQEqsoBL/rQSKpHiydC0YIEG0zSURYjWzr9cPRZs98FkYIOFIEtfuK83OWFB3/5nINLXghBlhqMe5SlJHY0jX8j6iYD4H+jLd533Dl6dFS9dE55GO53MRH/rKTXfOnkNgtepe7DEYIsNRj3KIv2qwyap7yom/MAeRkW71v+LyMj/bE/OOV9MQTtm6jt2lj5b8+fNemiA3gS87sYX6GEoyxVeGWaBtY/zS3FeZPucTQ7/m4frCcRE8iM4/VDDyU/gRYk3GAYI0EIU/Ccm09Xs4sw4whqo3v9c42ISy9I3q2eaVfv+V5oQYo1WC7Pr3A4giaM9smf01eBQOaU4HwOslmD0eAIerD1DCzjXl3ELoFYbNhg1h5ZtXUgFsdpMOSRVVsHYnGcBkMdWbV5IBbHaTDUkVWbB2JxnAZDHVm1eSAWx2kw1JFV6749SwjE4jgNxgi6fLQEtP9e2MJTb923ZwmB9Ag2GD/KCnrXxc/jW88mLZ7mqZfWR9GKQDQWGkwAZQV5/waHVKn8Y8FL6aNoVSCw3GACKCvoCeqvmcBY+dZ9e5YQCCw3mADKCnKn/ryEZuB8ey5byJ48IuKwsLhAYLnBBFB4C8rLy2tO/QkeNIk0TWo2syePcC+bKVsgQDQYP8oKetSAefXdWJpGsdxrMSj6EkM2mDl26yetmee+KT3dIz3dZoHAYoMJYceTWQ52/Q+Yv54U/xwkEnueDnV/7Mvmv1wRQVVJMz8ngAhCQAQhcGxBv02dwP1ka4naJEjw6uVV83yvfm972FKZ8tQwQYJXL6+aJ2If9e2xCX5XjFoliHvWc3HPzjRuVT95B5+lbkRcH6F2CeJy+gRNZNWTSeKWAfwgootsrRPEc6petTy32vYY7HVIpjw1UVAX86LqeSpysgvkylMrBYmk1glKNi8ighAQQQiIIAREEAIiCAERhIAIQkAEISCCEBBBCIggBEQQAiIIARGEwI6C7NNHUSz2E2SvPooisZ8gu/RRFI/9BNmjj6IE7CfIHn0ULSF8tXCF8oi91nNxu0Caxp5KBUIgfLVwhfKgBJm12LWLNFFtlAqEgHu1cD32E2TzFkPAvVq44nlQgmzeYgi4VwsvVjejqe9haSFr8iD3YrZuMRTcq4U/uEfzgt22IAvXd1cmEAaoY/NisW4vZuvru2NADj0jIIIQkGPz4iGCEBBBCIggBEQQAiIIARGEIC7k1H9EjIVSHacQ1KT1UI/3JS7sFIIe18ANz2vSFnYKQfRZ2FF7+apuf7GvxIo8tUWQJ/WVOvQ4T0128zEDW16Rnqe2CGqwcNfIwXzX7GpxFGBdjPQ8tUVQ0LwXN5bxVOTSPzWet/wLulMIEsqjfZza+2cMkp6ntguC9W3eW9L8sPQ8tV4QfP9a4t+WF3ZyQWhql6DszLKsKZu4F9Agggy8ETxqWNj6iOk2y1PTBKkL4LkvoUDMBeGsy1PTBPmWwJe3oMDiefOy5qlpguZ2yQL4pf8sY0Ehe2Q1WKk8NU0Q7D8CkLnZOIBXsQ9zbL6BYkOG1ThBwHvoOfllK9ZX63q58hxZtZ8gR+zl6lCCzHq5XrdvDzMankPP9hPE7eVaHML0UWzip1QgadhPkEAvV+UCScOOb9L817EgghAQQQiIIAREEILk+HvXL0nj9r1nFBAkKc8d5nuc5TwSBe1t1kTlosc4YYKnzlTUrNlv0p4UL4/Zc1sI2ozFUh6JggDOGT8qdj+mn9hs+BmroKmhbvjX+okvxhiKGvAdnbEeY564VPb+suEnkYn6q0zd9NYXTP2Avb/rjrFeIggBEYSACEJABCEgghBIFlRoHE127R39xC9f6ic08w11287rJ/5vu6ForrjrHYjOk/o7e1/8lr7gM/3gpiWL9AUZ+hYtX4CxXsmCnAUiCAERhIAIQkAEISCCEBBBCIggBPIJ0pj9sq9HzNjXtgI/k1RBe4MDN1YvmRfLV0p9xO1gnJ2vWiYEVn2yu3oiTyWTKTnYM7YEmUmioHzfq4XBZ6qW5LjH8pQCrAwJMMzOVy0TAqsuDThdNjDLrJLJdCLgdmHkEmQmiYJSqc1lSVKVgvtdN8WalwJ93n2AYXa+apkQWPVu6ityWalZJZMpcx3A9lhkJomCVi4E+HhSlYKx+9JjzUtpbgQYZuetlgeBVa8dEe45tti88gb7TT+/615kJomCltPrTTA9TnsZKEHcUmMYtoK3Wh4EVp2kvlg08i3zSlZQWlCa0IImJAraQa1z2SLT4yh1gEejSG6pMQxbwVstDwKr3ppAX/zLvJLOpI0bfltwQRMSBeX65JaG/q9aEbUF8ZSyYdgK3mp5EFj1dZ+LRUPXmlfSmTIjdMILmpC6m88IC323egkliKdUvzmzFXzVMiGw6i+CfKeWm1fSmWbVd3V1TUBmIp+kERBBCIggBEQQAiIIARGEgAhCQAQhIIIQEEEIiCAERBACIggBEYSACEJABCEgghAQQQjkEzTE/QmVu/uUA9GyrdE6ZMoj5xaURw/sXnRZxjVahyx5ZBfkQMiSR3ZBOdHH+oappyxp0/s6bGoZNFuZTr+2y6OEoIZXHjROgpfX5YTlFseskPEJ7JFHCUEDATqehy2JiwP69QsdIuMT2COPEoKepQJdoQKtWQVQWijjE9gjj5KCToXklkV+J+MT2COPkoJgWyufGTKu3y55yCdpBEQQAiIIARGEgAhCQAQhIIIQEEEIiCAERBACIggBEYSACEJABCEgghAQQQiIIAT/D92NOtkmQE+IAAAAAElFTkSuQmCC" /><!-- --></p> +<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> +<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>mm.L4 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> +<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L4"</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span> +<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a> <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> -<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:49 2024 -## Date of summary: Thu Dec 19 10:28:49 2024 +## Date of fit: Sun Feb 16 14:25:40 2025 +## Date of summary: Sun Feb 16 14:25:40 2025 ## ## Equations: -## d_parent/dt = - k_parent * parent +## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.018 s +## Fitted using 142 model solutions performed in 0.096 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.6 state -## k_parent 0.1 deparm +## value type +## parent_0 96.6 state +## k_parent_sink 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 96.600000 -Inf Inf -## log_k_parent -2.302585 -Inf Inf +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 47.12133 47.35966 -20.56067 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 96.440 1.69900 92.070 100.800 -## log_k_parent -5.030 0.07059 -5.211 -4.848 -## sigma 3.162 0.79050 1.130 5.194 +## Estimate Std. Error Lower Upper +## parent_0 96.440 1.69900 92.070 100.800 +## log_k_parent_sink -5.030 0.07059 -5.211 -4.848 +## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent sigma -## parent_0 1.000e+00 5.938e-01 3.387e-07 -## log_k_parent 5.938e-01 1.000e+00 5.830e-07 -## sigma 3.387e-07 5.830e-07 1.000e+00 +## parent_0 log_k_parent_sink sigma +## parent_0 1.000e+00 5.938e-01 3.430e-07 +## log_k_parent_sink 5.938e-01 1.000e+00 5.885e-07 +## sigma 3.430e-07 5.885e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 -## k_parent 0.006541 14.17 1.578e-05 0.005455 7.842e-03 -## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 +## k_parent_sink 0.006541 14.17 1.578e-05 0.005455 7.842e-03 +## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df ## All data 3.287 2 6 ## parent 3.287 2 6 ## +## Resulting formation fractions: +## ff +## parent_sink 1 +## ## Estimated disappearance times: ## DT50 DT90 ## parent 106 352</code></pre> -<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:49 2024 -## Date of summary: Thu Dec 19 10:28:49 2024 +## Date of fit: Sun Feb 16 14:25:40 2025 +## Date of summary: Sun Feb 16 14:25:40 2025 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.027 s +## Fitted using 224 model solutions performed in 0.151 s ## ## Error model: Constant variance ## @@ -1075,11 +1079,6 @@ negligible.</p> ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 40.37255 40.69032 -16.18628 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 99.1400 1.2670 95.6300 102.7000 @@ -1089,10 +1088,10 @@ negligible.</p> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.169e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 4.910e-08 -## sigma -2.456e-07 2.169e-08 4.910e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.447e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.198e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 4.923e-08 +## sigma -2.447e-07 2.198e-08 4.923e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1115,7 +1114,7 @@ negligible.</p> </div> <div id="references" class="section level1 unnumbered"> <h1 class="unnumbered">References</h1> -<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0"> +<div id="refs" class="references csl-bib-body hanging-indent"> <div id="ref-ranke2014" class="csl-entry"> Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker |