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diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index bd1fa16e..2b970d1b 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -364,7 +364,7 @@ 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 2023-05-18)</h4> +<h4 class="date">Last change 18 May 2023 (rebuilt 2025-02-16)</h4> <div id="TOC"> @@ -408,66 +408,67 @@ 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" 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>## mkin version used for fitting: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:11 2023 -## Date of summary: Thu May 18 11:38:11 2023 +<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: 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 0.011 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 @@ -494,34 +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" 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> -<p><img 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" /><!-- --></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" 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,iVBORw0KGgoAAAANSUhEUgAAAkAAAAHgCAMAAAB6sCJ3AAAC/VBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///+7nL8pAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAW6UlEQVR4nO3deXjU5KLH8ZTdspRSabEsSgHZKoIglMWjCAhVuCi1goKgQhWUTcoiCEeFqyA9IFU5RRaRCrIUZClFqSLniCxaEUGFoy1y8YhQ2Xe6TJ6bZFoYsNOZvL9kMk1/nz+StjNv3jzM96GdTCYjyUQAyeodoJKNARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARHE4IBebE220OmoNQFFvZ9BdtDoe4sC2mHs9sgiLRgQIRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRiQTX0UZ7RfipyHAdlUbNw8Y7VeWeQ8DMimYot+vA3fIAOyKQZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEKMC+u6b4jfIgGzKqIAGPFz8BhmQTbk+3kcH1w2MWn/DHRx5fxlzJcf1u1xtyYBKKZfH+0jdSYcvbGqS4HJrprSpXkCTGcpXZ0c2rBQx1SHLtVbEBx+SF7cObL5I+XGdd7tKQbEn5ChJkk7fuEFXDMimXB7vERPU5W/Bp6/dmindNGRtfMDfZblPcML6kdIyJaA2vVddSiw/JW1UwFwloOD+W98s97x8IqbHUceNG3TFgGzK5fFumaGtumy+dmumFKMsx1Y9K8ckKV80HqcE1MIhnw+Zqnw3JFQJqKWSTa+O/BVWark83pF7tVX0xmu3ZkopynKftEtZnvtuQfl4JaDxsrxL2n78+PFk6bBc5yXllvgoBlRquTzeA/+hLs+E/HHt1kxpm7I8pWS0rUVAeHS4GtAsWV4hOe2V68yUGVCp5vJ4/xi6zCEf6vqCy63O/4F+kr46WX7YMeXRUAN6S5a3SNkFd6ij/snNgEox18c7o0P1iOA3cl1uzZQeU5YTA09tljJl+dItBQFlV1yo/HhyFwZE1z/epw7mX3drplRp2MaXykyUD5WP/XJdu8oPHtcCksdXmJY2NmCOS0BPNc/IK2KDVzEgmyr+SHSm9PFD1W9/XXmmtbxx5bYbPgie4gzIkRAZ2Ex9XnY1oC/qVz1T3AYZkE15CijDoA0yIJtiQARhQAQpPqDcQ1cM2iADsimeUEYQBkQQBkQQBkQQBkQQBkQQBkQQBkSQ2OkGfzhqVwZUqkw3+uOZ2xT94gcDIggDIggDIggDIggDIggDIggDIggDIggDIggDIoiZAeVnHnV7GwOyCXMCGrxVWcysJkn1Vrm5BwOyCXMCkpJkOUkauGZ9XJlNRd+DAdmEeQHdEad+9UJH15+fWFmo3hY92yO/ZV5AgevUr1Kruf58T2yhcrP1bI/8lnkBtUpUv3qtadH3qDJfz/bIb5kUUGiXuAdCsmTH0tBRRd+DAdmEOQGtSRjW4/YKy+UMqd3Fou/BgGzCxONA+VfkY+n5bm5kQDZh1ZFoBmQTDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDEjc8UMOq3fBegxI1NY7Q26t+XapT4gBCdp2S6osH4h61er9sBoDEtQjWV0eCTpv9Y5YjAEJCnVeOKL1Nxbvh9UYkKDww9oqco/F+2E1BiSon/rB6vIPYTlW74jFGJCgn8NGvfHaK7cusXo/rMaABOU/FRhYoWoL95fQKiUYkKA5fzsry46J0Vbvh9UYkKCWX6nL3JpHrN4RizEgQTVOaKtSf6U1BiSoifb83VH7oNU7YjEGJGjKo+qFR+a3s3o/rMaABF2Mbj17wWP191u9H1YrUQFdntW728Q/jd0RcetHPjPXzeWzShG/DyjtuUenFDzTyW7Wd+Wa0WFFf3YnWcPPA8rrd8fcVWNC07RvhvSPqly94ciWxu4JQfw8oPn3qq817bjlgvpNjdCUfHlH86DDxu4KIfw8oOi12ur+T9RlmQ/U5S/l+DvMj/h5QG13aasB2tlbZT5Vl2fKfG3srhDCzwPqu1Bb3bldXd7U8ndZPv94UKl/6uxP/Dygz247KMuO2S21qwVHx4T2fCz84bA8Y3eFEH4ekJwUGju8ddss7etvw2atXLaw/gdF3vG/F4zaNdLD3wOSj65I/KLwvTP7oqtUvruoz4/KnRFcu8r9ew3aN9LB7wO6Xn7RZ5AO7XZIzl8YlonsEQkpYQEV7fDN59TV/8YZuE3yji0CWtdLW317l4HbJO/oDOhmF9C8hgaU6jyxdOfdBm6TvKMzoKSkpLeq1B41a1LjuqnQvIYGlF1DO7c9foyB2yTv6P8V9ty9l5VlXo9noXmNPR/otRZfXDn6ct1S/xYJC+gPqHaKtlobDs1r8AllK1pVrPXMH4ZukrwiENBb2iqxHjRvcQHte6n/y7/o3WCpv86KRfQHNCRog7JMDTLtV9iM8Fc/nBQ2D9o8+Yr+gM7dJ9WIrCHdj13XxH1AGXWOKctDoTwqWCKIHAfaMn3UzH+D87oP6KVXtNXwBHAG8gmhA4mH98Hzug9osPOWN8fCc5APCAT0SS1JkrskYvO6D2jqi9pq0NzCH/CdD/5Mf0DJ5eKSJXlyAPZXrvuADtZUT1n9IjRb++63J6oFNpjn7sPDyXL6A2o2Wj6ufDM2Epq3mGdh68L7TOhVb4v29R91pp127O4wGpqLTKQ/oMA0LaC0ytC8xR0HOrX8jVUFz/HGaq9OnK75f9BkZB79Ad31dy2g11pA83p5JLrTv7RVzEpoMjKP/oDeLz91u5S9sMIsaF4vA+roPFwQuwKajMwj8CwsMUSSpIoTsNcOvAzoxfHq8lzYIWgyMo/IcaDzu1ZsQS9x4GVAv4cnXJL33/c8OBuZRn9Aw7YbMa+3r8Zn9QmsEf5WrhFTkhkEXo2X6k8+AM/r/ekcuSfhycg8+gNy7IivL7WZDZ58U+IvMEVOYifV757UpGx3aF4GZBNiAZ1dNaBSWWheBmQTAgEdfrd7hfIPvJcNzVtcQLm7Vmbw1a8SQn9AraSKPd+H/7AtJqBdzVvFRrbBzxghX9Af0CNLzxgwr/uAjoZ9rCwX1z1rwCxkOt0B5Ry6ZMS87gOa6XyDcswiI6Yhs+kO6HLwaiPmdR/QkPe01YxxRkxDZtP/K2xMbyPeQeM+oDFvaKuxrxswC5lOf0Ar2tw5ftZbCmhe9wFtaayeC3Syrrc7RpbSH1CtQtC8xTwLG9o8efvCBi9DmydfMe/yLlf+LO65fnHHgdL6tu+/Ve90ZA2T3tZzeGL9AEmq2HCCu09D4pFomzDnbT27A+sOnZO8JPGFiGA3H4vNgGzCnLf13Bdd8Gau3Me7uv48/9esApVfz8r6TfnJ2SyuS/S6mSlv66mWUvjVl9Vdf741olCZGhERDQ7L8rMRXJfodYgpb+tpM6zwq1fcfKQff4XZhDlv60kJiF64ff+BnUv6lE0p+h6+CsjxYa+7+n7pm7lKJZPe1pPaWVIF3J/m5g4+Cij/kQ4ff7uoPo9qm8a0t/Wc/CE9fe8Jtzf7KKAV7dXz8Y/xEuSm8fO39aAGOacZ8k+fzFYaCR6JPrDJ/X8uXvFRQDHOP8HGTffJbKWR/oAOd4uXV5eVbnZzhNBLPgpokvMyVfeu98lspZH+gB4OXyu37H6wW09oXh8FdCg0VZbzZjQv+jNaCKc/oBrvyUekdHlZTWheXz2N39b0zv+5rRvfWm8a/QFVXy4vqnRJ3lAFmtdnBxJzv12Hv4+W3NIf0AMdPrujt3z+oTbQvDwSbRP6A9oTJlXbLd9eYQM0LwOyCYGn8RczlKfwK3/G5mVANiFyHOjEttXfYNep/0tA/4qJ7DaPn8ZcAukPKH9MJUmSqoOH5q4PaHrE4n2bunTjZYBKHv0BvRIwbt+pH18qg11p/LqADtVUP+krv8tCaJNkBf0BNXIe3B1n4HWiFz2prZY+Bm2SxOXM6dri8QyRkQIHEtdpq9Qgkemuui6gxOHaKi0a2iQJu9i+1+bvk2qLPLEReCnjOW31fGeB2a65LqB05zGll+OhTZKwhBh1mXmzwEVXdAa0Z8+ezSH91u5c+3iAu1PFvHNdQHltJuco/6eFunsPEJms6yZt9dA6/UN1BiS50D+Zi+ufhR3pHd6jeTOeeWqVKOeVd/t/qH+ozoAyXeifzMWNBxJ/3fQ9DwNZ5ul/qMu8ht/pH6r7b6C8959u37zXfPTR5pFof7Kn1lblL+nh3QSG6g1ob0spIrpXQ6kjeIYEA/IrnzZo2T3sCZGzTHUGdL5B08/V9dYmodiF7hiQf8nZ/clvQgN1BjQx6Ffnt78GTRSarxADsgmdAd3zQuH3I1pD8zIgm9AZUNCCwu/frwrNy4BsQmdAEVdfhH+zITQvAyrOyQtW74HXdAbUp0vh910fheZlQO4taxBcJeorq/fCSzoD+jRgjvPbxIDN0LwMyK23I3fJ+Sm1SkhBeo8DjZCiV//4w+oe0ghsXgbkTk7IL+rqo66e7ugfdB+J/rCW+jpYWDI4r+8COrmv5PxBofqpibY6gz1J8Rn9p3Pk7k9ZvR8++dRXAf2nW3BklSHgG/l96kAjbXUSO9/KZ8y7zG/xfBTQn7XfyZPPDetUgj49Kq/WXnW1AHvruF4/vzNlxRWRgTYPaJp29pujVbpPZjPGkvqz306aGirwyri4aWHPvxrdSOTDAWweUOxKbRU/0yezGeN0+0rlylcc5Mv/NNc3Va/3tKyRwDUobB5Q/w+01fNzfDKbMfoNzclxnLpntg+njHE+J4oS+HwAmwe0oId6Jb7zdff6ZDZDnArSnjV+08yHc7bbqa2eXKJ/qM0DyomK/e7klrbDPN/Tb3zvvPztpUo+nLP3Cm11j8CfijYPSL407Y7gdslGfMKZr/weqv31k1XXh3Muv+ucstxU77L+oXYPqARqr53xMGS0D6d0jKz3ytwna4u8esKA/M5P9QZ+tLBzlBEfbey9b6YMfUdoRgbkf87P6vv0kpJy6JMBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEcRfAjqxk5/tXiL5R0DHBwa3q9N2t7FTkC/4RUCOTiMvyI6lYWLXSicr+UVAW1to7z0eO8HYOcgH/CKgt53Xv9/4oLFzkA/4RUDzB2mr5THGzkE+4BcBZYapV8F0PJRk7BzkA34RkDyp+dr/7nikg9BVHslS/hGQvKFr7bYz2U8J5CcBUUnFgAjCgAhiZkD5mUfd3saAbMKcgAarFxyeWU2S6q1ycw8GZBPmBCQlyXKSNHDN+rgym4q+BwOyCfMCuiNO/eqFjkXfgwHZhHkBBa5Tv0qtVvQ9GJBNmBdQq0T1q9eaFn0PBmQTJgUU2iXugZAs2bE0dJTrz/8dUajMLD3bI79lTkBrEob1uL3CcjlDanfR9ee5WYVa7NCzPfJbJh4Hyr8iH0t3d73sKAZkD6YeiZ6Q7fYmBmQTpgYkHXB7EwOyCQZEEAZEEFMD+uyC25sYkE1YdToHA7IJBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQmwR0evYz47YaukXyjj0C+ir8qQXTGw1ydyUHMo8tAsq5LU1ZXu7Ei1b5ni0C+vJubZXa1cBtkndsEVCK83Oi9kUauE3yji0C+rq5tlrZ08BtkndsEVB+i7nKMrvZGgO3Sd6xRUDyf1rcOykubKqRmyTv2CMgOW/dtKQsQ7dI3rFJQGQVBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQywKaMO96vZ8YAHmgLzY+OgYb3+thbPwjPbHxsd2x8Y8/Ok9IbYsCmh93g0oNmkIC62Hjg8Kx8SGh2PhaNbDxdapi42+reuMj4p0RZ6wJ6C8a/YyN77wFGz8gGRs/4Q1sfOJwbPyaR7Dxu9pi4z1iQMVjQB4woOIxIA8YUPEYkAcMqHgMyAMGVDwG5AEDKh4D8oABFY8BeWB2QE0PYuO7fYmNH7QcG/9yAjb+n6Ox8RtisfHfdsDGe2R2QMfB8Scc2PgzOdj485ew8ZfPYePzTmHjHSew8R6ZHRDZHAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiiLkBpdwd1Pk78eFrJNVg0eHp67CdcI4X3Ym321VpPDNXfP7C8YLznxtVv3LrVbL4/F4yNaDUgKGrelQ+LDw+ITRJsVVwdH7beGgnCsYL7sRU6cXUCeUmC89/dbzg/AOqzk57SkrHHwQPTA2ocw9Zvlh3ovD4YV3E5/7t3b9J8cBOXB0vthNXqo1QlmNuyhOc/9p4sflPByyRZUfjQfiD4IGZAZ2UFirL5+oLb6BHnPjkaZ06VYoHdqJwvOBOZEmblWWKdFBw/qvjBef/+b5MZfm3x/AHwQMzA/pB2q4s5wRcEd3A7d3vqnznPOH5G8ZjO6GNF9yJy5mXleXomy4Jzn91PPCP4Ei7KRl/EDwwM6DPpP3KMln6U3B8foWQOWsHS8InlWoBADuhjUd24sNyY6F/BHW8+PxzKkmj8AfBEzMDSpcOKMsl0knB8VeWZynLgdXyBcdrAQA7oY0X34nsJ6VBucD8zvHi8x/8eGz5BPhB8MTMgPZKO5VlYkVsK2ukTMGRWgDATjh/hYnuxMbQ+muR+QvGC8+vGt3AoAfBPTMDOqE+E5CHNxAdfyxDPaN+vXRUcLwWALAT2njRndhYdph2Pr7o/IXjBedf9aA6bIF0AX0QPDH1afz9fWQ5N2K86PB0aamyfLae6Hjn/yDiO1HwK1BoJ3LDnyz4Smz+q+MF50+TvlaWz9SBHwRPTA0oreyr254IFn5nWF670KkbR5RZJTreGZD4TmjjBXfic2ncYtUlwfmvjhecP6dDxOJP4sskwQ+CJ+a+lLGqbVAX4Cj6xVFNqnbYJDy84G8Y4Z1wjhfbiSTJ6ajg/NfGC/4jnB3cuEob9T8v9EHwgC+mEoQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBefJowZuMpWlylQVW74z/YUCe7EhJSanbUVnsl/ulW70z/ocBeSOyn9V74LcYkDcKAqqu/AqrM7t9YMTcIz2r36pe+mJx68Dmi6zdN4sxIG+4BlR+yuc9A+rMTmtb6ZycWH5K2qiAuRbvnaUYkDdcA4qV5QPSSFlOlfadD5mq/HRIqLU7Zy0G5A3XgGbIcp60TK1ozy5p+/Hjx5Ml0z5HoARgQN5wDShBDShFC2hFwRP8vRbvnpUYkDfcBLRFyrZ4x6zHgLzhJqDsiurnUEwGPhKm5GNA3nATkDy+wrS0sQFzLN47SzEgb7gLyJEQGdgsyeKdsxYDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIsj/A4NB6COn/MNOAAAAAElFTkSuQmCC" /><!-- --></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" 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> +<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" 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> -<p><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" 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> +<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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6DDun856gAhAU3fShSqvult2GB+gMgzSLE5zWp972IweXPuCPB6ZnkzAQFFbCTK4QYq10Yu5gmoryQrjaiV814wYTK6AfR0qdeXUVmKtUO+dnrOE9AfZJ17D71+zjwOXTUgQNbbFZmdvD/hOO58Bk9AOVlXKj97bJ0mRVcNCNDLpLu88Z6eegOaErlbYUs0Lw9dNTxAGlJvQLRz06dXyU1xB10BkFaALAOdgzJn7EZVAKQdICJvc/uuaAqAtAZEFHIbTQEQD0AvMg+iKgDSHhA1Goo77AGIByCTVf3RFQBpD4iKpNNQFgBpD4j6rMGroACIByCLcVVoC4C0B0TPMn9AXQCkPSD6ZZVqJxYgN4r80R/FAdA7/4hlt+ZEH425vu327KAdaA6A3jnpjlYUnqwa1Pfqh+YA6N1zJNZfop61IUGCJw4B6N1jkd1uUPUKtwLVAdC754CT5KJqOWIMmgMgbXIr3LGE6D9GZ9AcAGkTi2zZGCfnMcU2aA6AtMqFDq3bTcXsiQCkLSAamIu7EwGIByAynYPWAIgHoFBFW9QGQNoDog/tD6E3ANIeEB3Fe3wAiA+gjMXzUBwA8fjhi9IENAdAPLIxGw9pABCPWPT4BdUBEI9ckuFsNADxSXnYapQHQDzywBTlARCP2BS2RnsAxCPTMpuiPgDikdG53dEfAPHIx0fRHwDxyIomLigQgHjkYOYzNAhAPNLILwMVAhCPfIq3QAEQDoMASG+AqEQagBIBiEfuRuCimGECsgu1FQIQ7VuIue8MD9Csqb4SjlvUeae/zgF5j9uIGg0NUMH0uEkjY1x89tn3jtc1IDoRtRY9GhigTYkvL1NZLxugc0D0vWIWijQsQMvNatZ2OOseEB0pw2VVwwIkn1yzdrqbAIBoeDKaNChAZpLE0TPT3XNcQgaZCQGou/Ep9fLmg/t9HFCqAYzCOvbiVJHEupIQgChhVFslI8+sU5XJcTizaACAiGxL3NwCPOp82Lai98tI3vPYe63yQPq0qWrqzZhCtGoIgFSJnlZnMlUT25oMbvSev6v//Ixc9fyJNBuvZRE/oJglAZS2gOPC+wizC1Pmkz3S/eqVcZiFXPSARnLZabQqqmrYsTEuQgGyy45STxxkLbuAWsUOyDeFKI1Tval7Z5lQgOhERUQ0kUWKJ1oVPSCnfKKDnGpA7RouGCBqNt3x6sm5a9LQqugB9VxpR9bhY5VrKd2EA0QjFP2/vYxODQBQutTvyOVtip8vpwTdFRAQfWmMe6TZBuRYK5q2mzapQn0mMXcECQnIJHk83sLCNCBzc/M+RlFet57PiOuoeUtv/1ZbZ775xXC6AUR2a7ajU8Z3YZ3WqN4zYOnJ77FQHQGiYHvcZc84oKjqy6OlCiYB0WMZppFmHFD1yWWfDWwCon/gOQ22AV2NLFf+2THyKKOA6K7vCfTKMKAVm7gKvwou1oFVQHTPGPcEMQyIaEKg17/u8PxeXQKi9gsxCzDLgGjWQd7fq1NA3nmYOYhhQGc7cBz18GEYEGUsOY1qWQUUM6ZFDEfrJc0ZBkShM/6GbhkFdP2vNIQjOubHMiDqq8hHuWwCmu6qBuQ6mGlA1EzaBe0yCWj+DTWgex+wDYjWyjANMJOAKoM+n8kFjz5+i3FAdHPUAfTL4ijMx4njuEU7TVgHRAPD1qFgBgGRw2cjJoTy/F4hANFvfh5omDlAk2e+j+/VFaBXT0GfTMVFDeYARXG+693ZBGS1x/lPWeW1PjA5GtsVHTMGyKRVO19OvusEe4DcZbts7c5GVNX6yOLjPBuUzNoxEFHB88WD8pgDNFw9MNziVJuM5fgQa7TMHKBDHzVeOog5QHEJ6sX8h7U/tNn7CJNwsgVo1k95x4NWPQlmDpCi+pnmoTmvjhmzO0EQS4DKuEULKm1ZPIg+rP6dL5zrjLwcfvwzBDEE6MzmKYwO4y/KvrGgAPlvdT+fMtQLRTMDyDvhvQyMdTKM/zU1XBr15PX/blZnnUTTrACy6Z3PLCDl7mr/737sUZiCqlnZhV07bMIuoDclWn4NXTMCaIR87pxbfZQREyCKnm88+7jfEzw5r39AHWoiKkAZTaSP7HKKW6BxvQN6PxEa0K4/2sq9TDL6laByBgCx/ljP72RPG1qdetWiRRUq1zsgETzW83oO31QO0Xosm+SDyvUNSBSP9byWe5Po0sMTq8IxBbDeAYnjsZ66GSKbPUq+3Ff6b9wfpG9AInmsp05eZJYNnm00P3VZ7CGUrl9AYnms59U8P0ndiyxN5l7+pzwUresVkGge63klC79WL5JG0o0ml1C7Xkdhonmsp3ZM56kXj5SH/k/DrNC7PgGJ57GeWjFfqQJv22GL8s/migIUr09AyrgP8xAXILslITnrPvSrvihfmrkbzesN0Ky97Sh/EOcYLypAZLNRPiq29OVf2kpHoHp9AeqpKKXCPP+9C8QF6NXER2H+IH0BqnhC6zg3mpcpZkBUtDgJt3boB5DzN/Tz0q5UbvS2o45QW4YBkW32eJyU1gugValjrxwmh/NyjQdKU30lyrF+553+rAIim+96RaN/PQCKl3HLC+gvx8s1bFMwPW7SyBgXn332veNZBUQWSX4JAKCHYXz3ZsohfBuNr0belNi9esV62QBmARE17/crBAgPiDy65D/UPG3KcrOatR3ODAOis9K7ICA0IItrSzmOcw7UtI18cs3a6W4sA6KCuP4wIDCg05IvDkY/TvlK0719ZpLE0TPT3XNcQgaZMQ2ILhS2t4MCQQFFHFMvvtB4Q1nHXuo3XkpiXYltQORwpng/GAgJqKL6xoiOkZo3sy1xcwuoe8HMum9NBrMCiExuhOFRDSEB9eykXjzoVY9tS+vMAbMjzP5lvjrCzr9BjLQcEAQCFB8fv9ZpeGlO6TKJaz0258YS67swVS6em4PrGsIA4mpFw2YTG1eH69G4sQgAUfAaU0znKgggq1rRsNnW6ZxxS2W4Ji1bigEQ2Tya6w8MghwDWVY+WpK7sNFbXgnobjz0gHh2Yaqckg2DBgEABRRy9okLO3PFCZq3855jVCUqQNQlCgdCugfkENbke9Wy7WLp2ya6axuXeEJMgCi413lcntc1oKmRRdUrRZFT37Zt9HeOogJE3vsi4iFCt4Cy99WsJRm/feu7XgfEBIgoX9YHJHQKKLJ1zVplOK/vZRMQuV9JzgAKHQKy//9F+COdDREQOfz3A3eo0B2gkB41awPGGyQgoieZLmChM0C7JSOrV3wkaw0UED27kozT0roCRElcYv7jknxPLokMFRA53L8eABk6AkR/76C6DiaLIcMFRBST6YPXa+gIEFmnm+Wn834HF9uAqKh4L17YqyNA7yeMAyLvnVGYgAGA+GTChqTu8AFA2md1cpOLAAJAPLJZGmgJIgCkfX6ITZ0GIwCkfUyaOwbiLiEA4pEDS3oUgQkAaR/LQMefcFYRgHjEak3xAUgBIB7/CX37P/buPCqq84zj+JWgF8FxAYUAAwgDKJtlLQgYNgEtiohsCgKyu7EYFFBRFFwAASUqiAuI4oYSjUo1iho0Km641aXaWm2qpSapyUmaNKnHzgwMBgRh3vdNSsPv88cVgTznOfd+DzMTnJmRNXg4hoAo7Nxujt+vIiCah2P3TRfoIBcERK7UL+keekFAFA43DpmEYhAQuXxn00/wiB4BUbjl88FxRIOAaO5MB2bMRDYIiJz/P99L7+R2bOnhWE8EhIC6kqL0pMOnQO/SL1wTuNYYASGgLqgrayW8+X5pn9WFrC43tYxHQAioS7pH9APGtPtco2koxx3vs2oEAkJAXRt9vW5w28+oSV8S296rHwJCQN2hl+TU5iG92k7pwzSvZwgIAXXLGBfTB+de/3Wis+SYftYeASGgbgo/Y9KQL/uLm5Zv1Pq/mwQKEBAC6rYd8wNftNybHrf6X4XWFwPj8CgMAclj66GQwc0/dHZm609zS+EQEAKSj4NSwXqut0JATBLqs3sYAkJA5DQ/aVzcHwEhIIrH9MrT3W4hIAREzvi3gZH9EBACIrf8dqDfXASEgMhZ1Nie6I+AEBBFQufLnZ4hIAREThQc8sROgICoz6O/bu8MSPygPj0trVKIgCjMm+WuxvM5Bgc8e2NAHCewK3Y/PwIBkdqaGBMfEGHlcsTIZnavDEhsoN/qPUsREJntqS0vcCrMdeutAXHcznibB18hIBJ5qrKPqs16b0Ac5/88cNNVAQKSm2WC7KML5r05II7T8djW97MRCEhOqmqpwSlZozdY+Xmp9u6AxO75mTy0R0Dy0bOWvCULrxZtx/X6gDgu+eTIZRPUEZBcdPc6OIxr/+y7c8oyOS+43sQiWMnA1REBdZ+mZ/NTfI3bPJL9TkVmYq97d9uN68wWGSKg7hFuKePLTkpel9KDx02YjOPtpLHnwxFQN5ya+lC1fuorBNSOYEau2aJHAgTUlbpM8eEmfwUBvWF8TUnS70oR0Nt5V0mOiu7GCKgD36+1WbxZhIDeouBTybFW/wsE1KERHsX6Gf0RUKdc+Iz1OhxX5bVuAQLqmGeDQUnFDQTUiWN5vLb4j8O2PALq9B51tYpJdNNMBNQhkb30dd2Fd4cioM7pxPmZRcYZIyD5IaAWuoOij776UIiAEBCxH2oGrD49QxMBISBiyacs9YeHaiIgBETMvqJg9YOrIgSEgIjtcN19VHHzcgSEgIiVvjiUd6LJHwEhIGLhlUPMwiq+Q0AIiJjOhPgYg/pQEQJCQMTm7hpgdjC4FAEhIGKT7t85+lHmMyECQkCkNA3/rWTjp5yMgBAQsdqbi0zrEh4XISAEREow99vCoJV77ukgIAREyvjuLPOgwm8GChEQAiJVdMU5bdr1UxuFCAgBkRq/OaNPntOPhiIEBPLSVvFxqzDmOMf99QVBxVsmzERAIIcq/cg7udElza8dmP/hnu1BaV9U7kBA0D1C0+l+AS+TQupbPzNmw18P2gb6uRpaICDoUv+yJvFRtNK07aeTK/cVJPrsi5iDgOCt7q+S/hF39s0vWRjezm48mvq3qHMICDoTO1F6S/V1TidfXxq1xckk5uKuq/4ICDowJ3FRPscdD1zxtm+yH3xylFn5b57rlSIgaMfcXH/Nbq2QoV19n2Dn5AWHTLScDgz+vToCglY7tg2Y7zt9XzejmBfVcNFIwfz0i+oiBARSmo//fFu+Kzaz+pLKAIX3l2WmjxuDgICMunbc8yF9y0qyn28eLURAQEY0Oz3zhEHZH7MvTO6ng4CAjPHc9D2RJWUG1zW+flSLgICMcM6VineXmJj5KB67dutjBARkxqc0ZUamJWp98Orl5FtFCAjI3Lj3zoKD24JGmufuaRo2Tx0BAZHa7yMa1oXZ5tRtGl4xeWMtAgKyu9lZVU81Ii1HJv5pTfw3lYZ/UEdAQCJ/7+FLJ32X2J5tDFPMfBo1ezwCAhIiz0dWL+MX9zlaVmcZ9o9wBASEkottl4wy9UBAQKY4U8hxWY13ERCQOG6kzulwnIcfAgIScRfvrygzy10/FgEBiRkxlhs0w380MUdAQOIvl9eLj4K6MAQEJPSU9C+EXiu2XIiAgETU9WTnUdn3v/wIAQGJH0ykv69viEdAQKT+SRYnuqS1AwEBEU2XwJHea0Zzv0xAIn9dBPSr49/+qR0/U0DzZrmr8XyOwQFPBPTr9vMEtDUxJj4gwsrliJHNbASEgOS2PbXl9WqEuW4ICAHJLU9V9lG12U8/32+IzMQanHsE1CnLBNlHF9r84sRxsEz5DJx7BNQpVbXU4JSs0Rus/LxUO/6OAQNx7hFQ5/SseQm1aDsOASEgErp7HRzGOXb6ZQSEgKggIASEgAABAQICBAQICAEhoF80oAPKbZ2Yr8hAZCqLKYpuuSymZG9is4wviyl3CpksU3iq3XWb/j8K6B2VdlZN6cvA9DwWU/rmGLGYEqPAZJmy91lMKfdmsox3YbvrljGzh/woDGHyViGTs3vQz+WoZT3o1j50FJNlrHvs7zAREAJCQAgIASEgBISAEBACQkAICAEhIAT0fxNQX08WU+LmM1lm4Vcspky4yGSZsC9ZTHm0ickyh6p7akDjmUwRFvWgZTTDmYxxFLCYInDsQcsAAAAAAAAAAAAAAAAAQHepFkyznks9JU76okRr6YY4XGGxUfMUyoXOmyskfSukXUY2hW6ZfGd3b6VYdteKLT21+Fgn73m0Y1z1h4oNo5qh7qPBYKOWKXQLHeM/1TswdQvlMq1T6JZRDKqxe8U7MLtWbFk7cZxFzCzaMQnU/2jqxtMnvAb1Rq1TqBYS5WWIjw/LNKmWeT2FapkiNSuOEyR9zuxaMaXLB4uPw91p5zip0E6wCwtbpUG9kWwK3UL2vOTtuVR5T6plWqfQLTNnu7b4+CSb2bViai+fIj4GqIko59RtWuidpkw5xECDxUbSKXQL6WjriI/1ZcZUy7ROoT87AruyCGbXiqlQPkt8jOD96caonzUJ2L+Wd2UQEPVG0ikMFro59QyD0yOZQr1MwCremdm1YsuBl7wDjBWvSzdGdM1efFyXp04fEPVG0inUC01axH8upF6meQr1Mp6Pz0x0ZXWt2BrHbxAfXXKYDIvjtekDot6o+SaMcqEqfff99Mu0TGFxduqnsL1WrDhK7uJz+6ZQjqntL/kX31H8UvqAqDeSTqFcqMorwZj+9Mim0C0Tu0byXw/ilzO6VoxF+3Gc0GgB9S1hpfh4upzBfSDqjVpuCGkWEtouYnB6WqfQLWPH/0d8fPc9VteKMTuvBsP5NrTPDNM01z9WlXE5lkVAtBtJp9AtdJc/6SFhTLVM6xS6ZcYsMfK4qnF5KKtrxVqsz7RR9P973MJ5RdCSCSwexlNv1DyFaqGhfLOlVMu8nkJ3dj5em6RgWcnuWgEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKHIlicR87s4hUE4HSCvgaqqqjG7xYcsztcBpwNIjPXFOQD6gMzEN2Hv1axMNHpRutisUfKaFh5KiSVNOD/w3/bukDWhKA7j8GknGea1mIZJLLYbBkMNBi8iMrCLeTBwYSwsDgzrSwaT0ezawA9hEMzW1EMAAACWSURBVKOwuI8w79gHuN6654knvvzKP50LAhqd9i9JZ5MOF2+hOzqlt8m3gSge0FUIN/ErhOv40Wxtz6+HhoEoHtAqhHp8ziu6f4/zLMum8dFCFA6olwdU/Q3o7u/AH1iIMgHN4tg2lA9o/Jp/MLFsG4hSAYWn3Tr9TCYGolxAlV7/4VizDwAAAAAAAAAAAAAAAADwP/wAIUlgvOGsD+cAAAAASUVORK5CYII=" /><!-- --></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 sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result -## is doubtful</code></pre> -<pre><code>## mkin version used for fitting: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:11 2023 -## Date of summary: Thu May 18 11:38:11 2023 +<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: 0.9.49.8 +## R version used for fitting: 4.4.2 +## 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 369 model solutions performed in 0.025 s +## Fitted using 342 model solutions performed in 0.242 s ## ## Error model: Constant variance ## @@ -542,29 +553,19 @@ checked.</p> ## Fixed parameter values: ## None ## -## -## Warning(s): -## Optimisation did not converge: -## false convergence (8) -## -## Results: -## -## AIC BIC logLik -## 95.88781 99.44929 -43.9439 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 92.47 1.2820 89.720 95.220 -## log_alpha 13.78 NaN NaN NaN -## log_beta 16.13 NaN NaN NaN -## sigma 2.78 0.4598 1.794 3.766 +## 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 1.0000000 NaN NaN 0.0001671 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma 0.0001671 NaN NaN 1.0000000 +## 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. @@ -572,9 +573,9 @@ checked.</p> ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 9.247e+01 NA NA 89.720 95.220 -## alpha 9.658e+05 NA NA NA NA -## beta 1.010e+07 NA NA NA NA -## sigma 2.780e+00 NA NA 1.794 3.766 +## 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 @@ -582,8 +583,8 @@ checked.</p> ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.25 24.08 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 @@ -618,21 +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="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb14-2"><a href="#cb14-2" 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="cb14-3"><a href="#cb14-3" 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="cb14-4"><a href="#cb14-4" 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="cb14-5"><a href="#cb14-5" 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="cb14-6"><a href="#cb14-6" 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 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="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" 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="cb15-2"><a href="#cb15-2" 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="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</span></code></pre></div> -<p><img 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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 @@ -652,22 +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="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" 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="cb16-2"><a href="#cb16-2" 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="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</span></code></pre></div> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<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><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: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:11 2023 -## Date of summary: Thu May 18 11:38:11 2023 +<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: 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 0.015 s +## Fitted using 239 model solutions performed in 0.167 s ## ## Error model: Constant variance ## @@ -688,11 +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 @@ -702,10 +702,10 @@ checked.</p> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09 -## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07 -## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00 +## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 +## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07 +## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.386e-07 +## sigma -7.436e-09 -1.617e-07 -1.386e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -733,15 +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="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" 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="cb19-2"><a href="#cb19-2" 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="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</span></code></pre></div> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<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><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: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:11 2023 -## Date of summary: Thu May 18 11:38:11 2023 +<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: 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 * @@ -750,7 +754,7 @@ order to explain the data.</p> ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.041 s +## Fitted using 572 model solutions performed in 0.399 s ## ## Error model: Constant variance ## @@ -768,31 +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 3.112 1.842e+03 -4353.0000 4359.0000 -## 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 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10 -## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05 -## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09 -## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09 -## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 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. @@ -800,7 +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 22.4800 5.553e-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 @@ -811,8 +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 0.03084 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> @@ -821,21 +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="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb22-2"><a href="#cb22-2" 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="cb22-3"><a href="#cb22-3" 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="cb22-4"><a href="#cb22-4" 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 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="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> -<span id="cb23-2"><a href="#cb23-2" 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="cb23-3"><a href="#cb23-3" 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="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div> -<p><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$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which @@ -850,11 +855,13 @@ as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> -<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:12 2023 -## Date of summary: Thu May 18 11:38:12 2023 +<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$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: 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 * @@ -863,7 +870,7 @@ summary and plot functions working on mkinfit objects.</p> ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.024 s +## Fitted using 373 model solutions performed in 0.258 s ## ## Error model: Constant variance ## @@ -881,31 +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 -9.664e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-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 -7.926e-07 -## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-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. @@ -924,8 +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 @@ -937,8 +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="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">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 src="data:image/png;base64,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" /><!-- --></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 @@ -955,99 +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="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb27-2"><a href="#cb27-2" 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="cb27-3"><a href="#cb27-3" 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="cb27-4"><a href="#cb27-4" 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> +<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="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> -<span id="cb28-2"><a href="#cb28-2" 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="cb28-3"><a href="#cb28-3" 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="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a> <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div> -<p><img src="data:image/png;base64,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" /><!-- --></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$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> -<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:12 2023 -## Date of summary: Thu May 18 11:38:12 2023 +<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$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: 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 0.01 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="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" 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>## mkin version used for fitting: 1.2.4 -## R version used for fitting: 4.3.0 -## Date of fit: Thu May 18 11:38:12 2023 -## Date of summary: Thu May 18 11:38:12 2023 +<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: 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 0.013 s +## Fitted using 224 model solutions performed in 0.151 s ## ## Error model: Constant variance ## @@ -1068,11 +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 @@ -1082,10 +1088,10 @@ negligible.</p> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08 -## sigma -2.468e-07 2.478e-08 5.211e-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. |