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Diffstat (limited to 'vignettes/FOCUS_L.html')
| -rw-r--r-- | vignettes/FOCUS_L.html | 132 |
1 files changed, 54 insertions, 78 deletions
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jQuery UI - v1.11.4 - 2016-01-05 * http://jqueryui.com * Includes: core.js, widget.js, mouse.js, position.js, draggable.js, droppable.js, resizable.js, selectable.js, sortable.js, accordion.js, autocomplete.js, button.js, dialog.js, menu.js, progressbar.js, selectmenu.js, slider.js, spinner.js, tabs.js, tooltip.js, effect.js, effect-blind.js, effect-bounce.js, effect-clip.js, effect-drop.js, effect-explode.js, effect-fade.js, effect-fold.js, effect-highlight.js, effect-puff.js, effect-pulsate.js, effect-scale.js, effect-shake.js, effect-size.js, effect-slide.js, effect-transfer.js @@ -309,7 +318,7 @@ float: none; self._setEventHandlers(); // Binding to the Window load event to make sure the correct scrollTop is calculated - $(window).load(function() { + $(window).on("load", function() { // Sets the active TOC item self._setActiveElement(true); @@ -1306,11 +1315,6 @@ color: #d14; </style> <style type="text/css">code{white-space: pre;}</style> -<style type="text/css"> - pre:not([class]) { - background-color: white; - } -</style> <script type="text/javascript"> if (window.hljs) { hljs.configure({languages: []}); @@ -1323,32 +1327,6 @@ if (window.hljs) { -<style type="text/css"> -h1 { - font-size: 34px; -} -h1.title { - font-size: 38px; -} -h2 { - font-size: 30px; -} -h3 { - font-size: 24px; -} -h4 { - font-size: 18px; -} -h5 { - font-size: 16px; -} -h6 { - font-size: 12px; -} -.table th:not([align]) { - text-align: left; -} -</style> @@ -1360,10 +1338,6 @@ h6 { margin-left: auto; margin-right: auto; } -code { - color: inherit; - background-color: rgba(0, 0, 0, 0.04); -} img { max-width:100%; } @@ -1379,6 +1353,9 @@ button.code-folding-btn:focus { summary { display: list-item; } +pre code { + padding: 0; +} </style> @@ -1391,7 +1368,6 @@ summary { max-height: 500px; min-height: 44px; overflow-y: auto; - background: white; border: 1px solid #ddd; border-radius: 4px; } @@ -1523,24 +1499,24 @@ div.tocify { <!-- setup 3col/9col grid for toc_float and main content --> -<div class="row-fluid"> -<div class="col-xs-12 col-sm-4 col-md-3"> +<div class="row"> +<div class="col-sm-12 col-md-4 col-lg-3"> <div id="TOC" class="tocify"> </div> </div> -<div class="toc-content col-xs-12 col-sm-8 col-md-9"> +<div class="toc-content col-sm-12 col-md-8 col-lg-9"> -<div class="fluid-row" id="header"> +<div id="header"> <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 17 November 2016 (rebuilt 2021-02-15)</h4> +<h4 class="date">Last change 17 November 2016 (rebuilt 2021-03-31)</h4> </div> @@ -1559,10 +1535,10 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation 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> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:19 2021 -## Date of summary: Mon Feb 15 14:11:19 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:37 2021 +## Date of summary: Wed Mar 31 19:01:37 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -1660,17 +1636,17 @@ summary(m.L1.SFO)</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: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:19 2021 -## Date of summary: Mon Feb 15 14:11:19 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:37 2021 +## Date of summary: Wed Mar 31 19:01:37 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 369 model solutions performed in 0.082 s +## Fitted using 369 model solutions performed in 0.083 s ## ## Error model: Constant variance ## @@ -1752,7 +1728,7 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> <pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - SFO")</code></pre> -<p><img 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" 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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 residual plot.</p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> <p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> @@ -1763,19 +1739,19 @@ plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, <pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> -<p><img 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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:19 2021 -## Date of summary: Mon Feb 15 14:11:19 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:37 2021 +## Date of summary: Wed Mar 31 19:01:37 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.05 s +## Fitted using 239 model solutions performed in 0.049 s ## ## Error model: Constant variance ## @@ -1841,12 +1817,12 @@ plot(m.L2.FOMC, show_residuals = TRUE, <pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> -<p><img 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" 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/weFs/r8wTdG17S8EFc9IKWijctHShjvYV1QStdUZIFFAEOCHpMaY6iF7RRtGlp6mPWW1gXtPZpJVlAEeCagsZ7dFpy6MzZIyue8Yy33sK6oE0PKckCigDXFJRuayVd9PJovUOmgXVBOyQoygLYo1jQrAuZSnM45Tpoyq+JiSdvmq1MzevPLHK/tY2e/0phFsAal75hOedPs35Cfsjr0MzrQ2sbDIxzIAtgiYsKuq1n12V0URDxmS5zZTPA6s3zY2YqygLY45qCriONO3tHB8QmTPJeYr2FdUGn2+goEjgF1xT00SGUfkreF5bGy9z/YV3QBUOVZAFFgGsK6red0hvSXS0J/tZbWBd05YtKsoAiwDUFrS58lzxClgtL82pZb2Fd0C1dlWQBRYBrCvq+z/B3KjeutCt5c9BE6y2sC/pdCyVZQBHgmoJmTapUfvD9AYSQLvest7Au6M91lWQBRYBrCmrkzArZ7mitC3qhigNZAEt+LR7BnLI2Hjd31iMf1gW9VVrbLEA9fyWxR+YwK8KXoDmetnsEAG4HX4LSUv9pmwboHc4EDbuobRqgdzgTNOqktmmA3uFM0CcOaJsG6B3OBO2CkeZAATgTtM8qbdMAvcOZoEM+1jYN0DucCTruPW3TAL3DmaDvx2ibBugdzgT9ZLC2aYDe4UzQL3trmwboHc4E3d5J2zRA73Am6MHHra4Gbgtngp6qo20aoHc4E/RyZW3TAL3DmaB3ArRNA/QOZ4LmemVpmwfoHM4EpYHm/Y0B94Y3QavKjboA3BPeBK13Qts8QOfwJuj/9mmbB+gcZw3kJSfoU5uU5gEujbMG8pITtN8XivIAV8dZA3nJCfrGfCV5gMvjrIG85ASdGKskD3B5HBL00qlCGhc+kJecoB+MtrMe4B44IOg3FQmhbWweigsfyEtO0M9et7Me4B4oF3Sl18CVhE72WGSjceEDeckJuraXnfUA90C5oA+PoMnCizFRtloXOpCXnKDftLezHuAeKBfUb4ck6A6ZzudNWB3I69RAE94fWd/ssMxXAuCmKBe0wRRJ0Gl2dIZ86w+z3hRvfL7IiM+n1jf5TaZPe+CmKBd0mXfsIXJ9SfE5tlqvbHaSXutKSMm5Mg3kDvFXK9pZD3APHDiLnx8kfLssMU5mCDmJeaTFNdq+8qcJY7xWWG8hJ+g9XzvrAe6BI9dB7x5du+eGzcbh4ym9RsTxYscpGshLwFvxWLnAlWHzS1LQBuGEiNwVlnaUtN5CVtBy15UkAq6OckG7mrDRuEfnTJpVcrewNF7hhXpa7U87CwJugXJBXxbpEVzsDRuNzwRHfbB3eqWle8d7y4wALytog2N2FgTcAkcP8XfbN7bV+o8hZaUr9XXWyjSQFbTVt3YWBNwCh7+D7iO2vyzeP394+yH5B4xkBX16g50FAbfAYUGX+9q6zlQosoK+vFRNWOBqKBd0lcTUkJaq8soK+pbMb6DAPVEuqI+EX9MzqvLKCvr2VFVxgYvB21Od9MMR2iYC+oY7QZe8om0ioG8UClouH6ryygoa/4yquMDFUChoXFzc3IDKw+dMrBmmbsgtWUET26iKC1wM5Yf4wU9mCNPsjoNU5ZUV9IdGquICF0O5oJUNTxltqqQqr6ygf1ZRdX0VuBgOCGq4B3l+FVV5ZQXNfVTmMTvgligX9PXSW4XpttKMDvF0Z437qiIDl0K5oKktSdmosqT1XVV55QWlbWUeVwLuiCPXQffMGD5rv8q8NgT9ueIdlcGB68DdhXqBF6ZpmwvoGIWCLk6ki02oymtL0PNB11TFBi6EQkEDetMyJlTltSUofXOYqtjAheDxEE9vlDunbTagWxwU9GyCyuFibApK3+mjLjpwGZQLeqndaLrBk5ST6TrZTmwLmlr5sKrowGVQLmiPSpto/Q7n29l67LhwbAtKVzXIVhUeuArKBS37Gb1KEumX5VXlLURQ2mqhqvDAVVAuaJk1dKlPOt2qbtjXwgQ9Xe6qqvjARVAuaPvHdz/Snd7tou62uMIEpaNfVhUfuAjKBf25Ail1nNYovrWQDRwbyCuPO6EYcw44dJkp7dhNStf9YbO1wwN5PWBNVFbaN3G7MuysD7gmbIahcXwgr3x0fDOi1aAnI7+3s0DgkrAZhsbxgbzysb/YOmG6vYLtrkiBa8NmGBrHB/LKx6yG3cXZSx/bWSFwRdgMQ+P4QF75GDL/kdXCbA56cnBn2AxD4/hAXvmY+Pbxiv9QOna6nRUCV4TRMDQOD+SVjx+rfBBSovHsSoWdkQFXhtEwNDIDee0hecwsNGNuDZ83QtuXsGM8JuC6sBmGxsQm2U5u7fgE3dbwUHRzvx110OWyO8NmGJq8jXbLvWOHoKPED9mY7u9MtCsVcE2UCpq6c+1F4ej79+l9XWw03tjXAGnTt6/1FnYIOnSBMEmv1XeUnRUCV0ShoL+FCYf3Ib9UEU+AbDTe7kcaNhUgtZs2td7CDkE/k/q5+6k4ulx2ZxQK2i1o5cmvyldutmbXUZsdN5xt2OR3qvIQfy/yvfs0Y0qFxuhpxI1RKGh58XvhTHKh0Ob3YwI+VSkovdg98NEyPS93m2RnicAFUSgoEcc92mDXE577wjr9o05QSlOO36L0eqhsFODyKBVU/GFok32PIN96vpxaQQ3sq4SOHNwWhoJS+tXw3+XeUiIoHdcJfYa6K0wFtYEiQe83LeRnK+CyKBXU28fHx5tIQyWpyqtIUHohZI+qbEC3KBR0Uj5U5VUmKD1QMUlVOqBXuOybyQof1bunbQFAH+hFUPoqumtyS3QjaHrj2dpWAHSBbgSlF0MStS0B6AH9CEr3VTitbQ1AB+hIUPpV2CVtiwD8oydB6XtRt7StAnCPrgSlb7bK1LYMwDv6EjT76T74Vd690Jeg9F6TydrWAThHZ4LS6zVxOdSt0Jug9Eq1WZoWAvhGd4LSSxEYbNaN0J+g9ELVzzQsBPCNDgWlf4Su1q4QwDd6FJT+GrJKs0IA3+hSUHqmCnpzcBP0KSi9UD1Go0IA3+hUUHqt/jD8puQOsBNU5ThJhZHSbKA0nGfuxlHDVmFgT5eFkaAajJNUGHfbP5VK6e0nm82e164exk10VdgIqsk4SYWRFV33Io0eKB7pp/RQHQ3wCRtBNRknqXDmhv4Y9Le4kF4qVYNwgEPYCKrJOEl2sKOip2EhXO6rBNA5bATVZJwkezjmOVY8xKeWwlPzLgobQTUZJ8kuooOaT3s/MbqfNtEAdzA6i9dinCS7+K9KseKP+AYV3qMu0CfMroNaHSfp+ueLjPhodM9cTM/9LwWMHIpPUFeFlaDZ59OleXqBvmdPDTThrdGP6RWFs6Pfavcvie+gLgobQbMm+xLfseLvO8tlttPoEH/fW5zefdV7gybhAHewEfQDr1HxI7xeocwFpcbroH7BMRgLxCVhI2iN8cJkFdnMXtChA8TLTG8//Xf7pue0iQi4go2g/tvFad/wdOaC3m7ZdPb8tvWv0tx55T7EPSOuBxtBG48Up/8GD2UuKM39etSw1ZKZSe3q/6hRUMANbASdT4btyqB0u2f/GNaC5mddxWE2x78D+oPRZabYUkT8Sri1EilKQem156tvYxEXOA1W10EzkzLEWda3cdbfZyMopQm1Op1lExk4Bb0+8iHL/bnlhv3HKDYoelxOUOHcbEDIQvTS6Cq4oKCUnugcviKHYXxQdLikoJTufyJqM9MEoIhwUUEp3Vqv4UZ8iuoflxWU0sSmdb7IYp4FsMWFBRUUbVntE1y51zcuLSil3z9bbuzFIskE2ODiglL616ig5w4UUS6gPS4vKKWpC2o//JH5wydAJ7iBoAL7+5V5cS/O6fWIewhKacq8eg9NwFif+sNdBBU4FRPWYA7OmHSGGwlKac6e18o9Ngu95OgJtxJUIGvXwPINpx1H57d6wd0EFcjeM7J66KCteJJeF7ihoCK/z24V0Or9Yzix5x43FVQgdduw2uV6LTyFoz3XuK+gIldWvFa93DNzj0idPkypX607uhLnDfcWVOTv1dH1ApqP+TLY9+kBkZ4rnV0OKAgEFUn9Nja4WHCXKVsHFccNenwBQY34f3z564kdK3o8MvKLn9KcXQzIA4Ia8TLc8hT4yowX6vlG9piw8oeifjb04NtvLc0o4pz8A0GNlJkmTrO8donTs/HTejfwD2k5cGb8z0U0fkj2y5HvfNSjGm4XMAOCGhnk9welOW1L51t1KfHTUT2ifCs07T1u0TenGd+aP7+1+Om5rB6uehUEgppoUaxmY/+SP1i+cfX71dMHtKvlW7Zu18HvLE04dYNJ+mbfLnmuY8zf1U8xia5fIGgee/p1nWnrp6UbJ7Z8MvnlDnXKFQ9t0u3ViXNX7/7lb+2+Mz7UqN2ahHHBjXZqFlFvnJwzdaNlB5q6HUzWiWRePrLls9hhL7SMCvEuGdGkY583p8z7YsuBU1fuqAga1lqc7vZy10/Q3CEBQaWCIpPM1+t3MFk+uP3nkR0r5015s1/XJ+pUCigWGPHok117DxwbO2fR2m17jp29kGJvz+RhdcWzsfl+7trF6ewS0b9eXlUmwny9jgeT5ZDsm0k/7d365aIZk0YM7NW5ZcOaVQK9SWCFiLoNW7fv1Wfg0JhJM2YtWrRuXULid8eOJSVdSknJ60Qq/KWw0dPbRDXf5cz6nUjIc+L0N09zX3Q9mKwuyE35J+mXY7t3rlu1aMGM2JhRAwf26tWxbYuGDSMiQgMDixMSEBgYFhERUO3hsPC6rX069+rVf+DAgW/ECLw3QyBOHFZq2TqRjYkSB44ZOJ1k5GpKHmq+ZTgV7wRpVnqO2fqiHUz2h7YmvD5UEs+lSU1JuZSUNL/KlqOJCU83Fjz8QjBygejmeNHSQeKwUi/1EnnasO+aNzRQO8JISGAeASQ/3oEWhETIU6uhOh5r6zjFotq2FZzwM//gKtrBZFMTTUTuVxLPHVgY3LVveA9tH4++n2LB1SR5zhxTx5FEx4mokpB4mS7yvGb2JzhrMNmmh5XEcwtubFnprqfwAt/7hr67sIvf8+brnTWYLAQFBUmsUqa8X4xFz8NFO5jsAyAoMCP3/PF0y7XO+iUJggK7gKCAayAo4BpnCdq6pOUlOn/iwQyGoVmi07KLWf7jOorXH84RNN3yCl3Kl23OMyPsO2ahJ73KLPQfXsxCn+87jVno3ZFW/nUdxO7nGjQW1BpburGLHc6uR6Y5I5iFzvJiFppGf8ws9JlazELLA0HlgKDmQFDlQFBzIKhiIKg5ENR+IKgcENQcCKocCGoOBFUMBDUHgtoPBJUDgprjqoL+Gccu9hR2HYQc+JpZ6NzRzELT9UeYhf4vllloeYpAUAAcB4ICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAa9oLGNy7d6gSTyBuljqIGMIicuFmasSjdEJpF6QseC6g5SxxRT/uyTaHZ7XFZmAu6zWPI+o7+l1iEnh0cJ7BP+8A5TaTbjViUbgzNoPRYMnLbOK/JLMrOC81sj8vDXNBWHSlNC5vAInR0GxZR6eWP/0cki7QvPS+09qVnlhomTEf5Zmtf9oPQjPa4LVgLmkKWCNPB4SxidxzIIird0by5j2gRg9JNoRmUnkTEbvDjyXnty84LzWqP24K1oL+SQ8J0nodFB5EaUKNDA/96ixgEppGiRWxKl0IzKD3jnDi40wjfdO3LzgvNcI/LwlrQ3eSMMF1JGIzjllM8aN6mAWS29pENFrEpXQrNqvRVXmNY7XExNMM9LgtrQRPJWWG6gtga/ctBMteIw0P1L2VrGDkHkSxiU7oUmk3p1/uRl7PYlG0IzXCPy8Ja0JNEfEZmfglmCTaSc9oHlSxiU3rkg+eRNC59e3D4JsqmbGNoA0z2uCysBb3psUKYvlmNQeh/j4lDCG8h5gNIaIBkEZvSpdAsSt/uGS11tc2gbFNohntcFuaXmVo/Q2lWRAyDyIlktTAdVIVBaMPHHJPSjd8eNC89q1I/45LmZeeFZrjHZWEu6A7Pdw72CWTx+Hr2Y8Gx24cVW88gtEFQJqVLoRmU/i0Zu1wkXfuy80Iz3OOysP+pc32T0m3Y/NSZNrxWyccTWEQ2flFkUbohtPalxxlHobumfdkPQrPb47LgZhHANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcI3Ggm6aAYAdzLrnHEGb9osBoHDK/+IkQQ9rGw+4KHUhKOAZCAq4BoICroGggGsgKOAaCAq4BoICroGggGs4EvT4zIlrs7TNAnQPN4LmDK0ydnq7h//UNo2euLN3M4tRonQON4LGPXFXmC5spG0aHbG8YsunQvrednYZvMFK0Ozz0qCNNF1muEYLQZ/YLU5zI04ry+My7Ij4jdK01591dh28wUbQrMm+xHdstrC0XGY7C0Gr/iXN2u9UkseF6LhWnGZWuOTsQjiDjaAfeI2KH+H1ClUgaLO90qz6KSV5XIhww/fPdrucXAdvsBG0xnhhsopsViDogpbid4LF9XOV5HEh6v0ozR496uQ6eIONoP7bxWnf8HQzQX/Puw81MN5sk+xXq035qHvkb0rSSPz99TpXOPmdIB5v6MFQXGgrCBtBG48Up/8GDzUT9MpM0538JRZabPT91OHLM5RkEcmdGNyjZ8XBmUq3447bDbpvOxAbXLQjBOsANoLOJ8N2Ca5t9+wfI7NdwOdK4skzu8V14R+3x0htojmT+/M6Nx/6l7Or4A5Gl5liS5FzwmxrJcJY0Konxen1Mvr/CAVWYXUdNDNJOlpnfRtn/X2NBL3vbTipCv9Lk3CAO5z1S5JWn6ABKeI0q/QtbcIB3tC7oH2miNPPWmoTDXCH3gX9u3qfTTsGVVZ+dQroA70LStNmde807T+NggHu0L2gwLWBoIBrICjgGggKuIZvQVNP39U2LdAbPAt6pVfJhwP6/KNtYqAvOBY0rcY7GTRtfBR+ZndnFAq6MB+q8toh6K8d898AAAxySURBVOLu0qz9alWJgL5RKGi5fKjKa4egb8yXZrNGq0oE9A3Hh/iRH0izaRO0zQx0hcOCJr+tKq8dgm5uLD4Wer9uoqpEQN8oFzR39djRAp3Lqsprh6C5nTr/mHqk7dOq8gCdo1zQd0hN70pNQ/xXqMprz2Wm+3PqBtRfgKfI3BrlgoYPp593pmlNttlovHn0A6y3wC9JwC6UC1piKz1emdJNj9lonNiE+EQayb/+SFsTnrMdqRa4HcoFrfYhveNxne4PsNU6u0Vza6vv7U404vuZgiKB+6Jc0GGBcbRu9LletWw2X2hV0AfgEA/sQrmgt595lh7wJt7rbDa/vMN2OAhqwT/rF//o7Br4w8HroCm7LqjLC0HN+TC452s1Ol53dhm8wfEvSe7F+oevUJo1toOz63AiGTesrFQuaFcTqoqBoGa02SxOsyonObsQZ/FLa79yleNyzFcrF/RlkR7Bxd5QVQ4ENcPdO/A9XWFZFj312CTz9Y4e4u+2b6yqHghqRkNDh6mP/OTkOpxFn4/E6fUy5l3EOPwddB9R9X0egpoR21PsZSohwuIY5yZU/12atdxrtt5hQZf7quoLGYKakda6+fJNb1U44Ow6nEVNQ+cwzfeZrVcu6CqJqSHqukOCoObkrn6p+xRr57HuwSvvidPLgXfM1isX1EfCr+kZVfVAUFCApIozD323pc4s8/W4Dgr4YJGfZzHPzmnmqyEo4ILvQg/TzJTnXjJfz/FDc8Cd6PaFOL0XdNVsvUJB4+Li5gZUHj5nYs0wWzcsFw4EBQWIMPyE1tb8CTTlh/jBT4q9z2d3HKSqHggKChB1Qpo1PmS2XrmglQ1DcG2qpKoeCAoKMHKYOP25gnk/Mg4IOleaza9SyAaZN1JsvAtBQQGSq7925OynIRZ3GSsX9PXSW4XpttI2D/GXJoR7EFIicpzcKIUQFBQk9e0mtXr/bLFauaCpLUnZqLKkta2OEY/7hQ2Zt3LF/DciAi1TSkBQYBeOXAfdM2P4rP02G7fsZLzgmvVCW+stICiwCzYX6kvljWV8oEz+9XsDTXj4BgYGXaL0pUDMMbc1Vybo4kS62ISNxo2iTUtTCz4+fyvFSMC8lJTbworMFMwxtzGPUiZoQG9axoSNxvEenZYcOnP2yIpnPM3HhTeCQzywC0a/xW9rRUQ8Wss9fQxBgV04KOjZhJuFtE/5NTHxpHwjCArsQrmgl9qNphs8STmZ60d2AkGBXSgXtEelTbR+h/Pt8NgxKAKUC1r2M3qVJNIvy6vKC0GBXSgXtMwautQnnW612btdoUBQYBfKBW3/+O5HutO7XRqpygtBgV0oF/TnCqTUcVqj+FZVeSEosAsHLjOlHbtJ6bo/1OWFoMAuHLoOeumU6rw8CnrpsLt268ExDgj6TUVCaJv56vLyJ+jSAA8vj24YGJQzlAu60mvgSkIneyxSlZc7QdcVG0HpvjJNnF0HKIhyQR8eQZOFF2OiVOXlTtCw58XpOQ+V362BxigX1G+HJOgOf1V5uRPUe5c0C5zj5DpAQZQL2mCKJOi0uqrycido8U3SrJS6UcaB1igXdJl37CFyfUlxdR813AlaW+qt7zuPf51dCCiAA2fx84MIISXGqeoelD9BD3u2OnFjqncvZ9cBCuLIddC7R9fuUduRJXeC0mPVipGSU51dBTBDqaCpO9depDT379P7uqjKy5+ggEsUCvpbmHB4H/JLFfFxDgeyZSWZ8IegwB4UCtotaOXJr8pXbrZm11FbHTfIcSDCRLGZDmxujbPPBpftdEyjYIA7FApaXvRqJlE5DiLV7hD/Y/CCf28ur5igTTTAHQoFJWuFyQYNelvWStDWK8XpnpraRAPcoVRQ8TH3TfwImlMiXZoH/6NJOMAdOhc0q3i2NA+9pEk4wB06F5TWl35CPx2COzldFKWCevv4+HgTaagkVXntEjTp3dffL+R8bHPV/ZSeiIpTVQzgF4WCTsqHqrz2CPpphdGfjQhearvR5siQhyotV1UL4BiOx0k6XfGiMD0X/Gch7a5e1KQiwCUcCzp1nDR7S6tL+kCPcCzokE+k2UfDtc0MdAXHgr47QpoNxj3u7gzHgp4vL9b2Y/kr2mYGuoJjQWl8hb7TXqi4RdvEQF/wLChNXjx5ma3RwIDrw7WgALASNPu84S6O9GvW34egwC7YCJo12Zf4jhXv41gusx0EBXbBRtAPvEbFj/B6hUJQoBI2gtYYL0xWkc0QFKiEjaD+28Vp3/B0CArUwUbQxiPF6b/BQyEoUAcbQeeTYbsyKN3u2T+mwHbJ60z4fGqx0ZlP3t2O+45BQRhdZootRc4Js62VSIHtfullImC12Ra5MSFDJjzeSP3zosClYHUdNDMpQ5xlfStzr3vTw2Yrlja+JUxnPq4sDXB1mP6SNO667FsWgv7vG3Ga+9BZB/IA14WpoETeNgtBq/4lzdrvdCAPcF24EbTJfmlWy956gHvAjaBzOmQJ06/qqOt2FLgaTAXdfU/2LQtB7z9X54PFfcLUDfINXA5n3W5nISilu0a/ttCRLvOAK8ORoABYAkEB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVc4zRBxy2y4N0OfZnR+kVmoXt0ZRa6byt2obs8wyz0Cz0s/3EdpbKTBP18oCUtA2ozwzuSWegKZZmFruXBLHTtwIrMQkf4WvnXdZBht50jqDW2dGMXO/w8s9BzRjALneXFLDSN/phZ6DO1mIWWB4LKAUHNgaDKgaDmQFDFQFBzIKj9QFA5IKg5EFQ5ENQcCKoYCGoOBLUfCCoHBDXHVQVNeJpd7OrshkNeMIZZ6Gx/ZqHpsM+Yhf4zilloeYpA0Oxb7GInswudzvBJP4Zlp2awi82wbFmKQFAAHAeCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGvaCxjcu3eoEk8gbicgABpETN0szFqUbQrMofcFjATVniSMCaF+2KTS7PS4Lc0G3eQxZ39GfyV1xs4PjBPZpHzinyWhxxqJ0Y2gGpceSkdvGeU1mUXZeaGZ7XB7mgrbqSGla2AQWoaPbsIhKL3/8PyJZpH3peaG1Lz2z1DBhOso3W/uyH4RmtMdtwVrQFLJEmA4OZxG740AWUemO5s19RIsYlG4KzaD0JLJLmMaT89qXnRea1R63BWtBfyWHhOk8j0wGsWt0aOBfbxGDwDRStIhN6VJoBqVnnBPvVB7hm6592XmhGe5xWVgLupucEaYryQ3tQ+cUD5q3aQCZrX1kg0VsSpdCsyp9ldcYVntcDM1wj8vCWtBEaVi6FSRF+9CZa5KEaf9SDAajlyxiU7oUmk3p1/uRl7PYlG0IzXCPy8Ja0JPkiDCdX4JZgo3SCPYaI1nEpnTDIV5C49K3B4dvomzKNoY2wGSPy8Ja0JseK4Tpm9UYhP73mDgA6BZyTfvQkkVsSpdCsyh9u2d0ujhnULYpNMM9Lgvzy0ytn6E0KyKGQeREslqYDqrCILThY45J6cZvD5qXnlWpn3FJ87LzQjPc47IwF3SH5zsH+wSy6F8h+7Hg2O3Diq1nENogKJPSpdAMSv+WjF0ukq592XmhGe5xWdj/1Lm+Sek2bH7qTBteq+TjCSwiG78osijdEFr70uOIgWval/0gNLs9LgtuFgFcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQrelp7CiGTKcBi51djP6BoFpzOD4+PuwJYXKG9k50djH6B4KyIKq3sytwGSAoC4yClhEO8aEfNfOL+ORq1zIPiX1rLm/oV2epc2vTGRCUBfkF9X77264eoR/taOKTSud7v71juMcnTq5OV0BQFuQXtBelZ8lblG4jp+4GxQprXw92bnH6AoKyIL+gMynNJl+Klv58lBxKTk5eSZgMDOmiQFAW5Bd0tihovCToWuMFqJNOLk9PQFAWyAi6h1x3cmH6A4KyQEbQ6yXEYTQnF/2IrDoGgrJARlAaU3z6jjEe85xcna6AoCyQEzR3dpTfw3FOLk5fQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANf8Hpqlws4GXNvMAAAAASUVORK5CYII=" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1943,10 +1919,10 @@ plot(mm.L3)</code></pre> <p>The objects returned by mmkin are arranged like a matrix, with models 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> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -2051,17 +2027,17 @@ plot(mm.L4)</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 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> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.03 s +## Fitted using 142 model solutions performed in 0.031 s ## ## Error model: Constant variance ## @@ -2115,17 +2091,17 @@ plot(mm.L4)</code></pre> ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.046 s +## Fitted using 224 model solutions performed in 0.045 s ## ## Error model: Constant variance ## @@ -2222,7 +2198,7 @@ $(document).ready(function () { $(document).ready(function () { $('.tabset-dropdown > .nav-tabs > li').click(function () { - $(this).parent().toggleClass('nav-tabs-open') + $(this).parent().toggleClass('nav-tabs-open'); }); }); </script> |