diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2015-05-15 13:31:28 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2015-05-15 13:31:28 +0200 |
| commit | 89cd81cf47c9f05831e854d5185b5cc599745db4 (patch) | |
| tree | 53c6842bddb48a879cb909ed434078cf157af30e | |
| parent | 9a5d2378845e584a2a2c6c09f7ef99a82d09273b (diff) | |
Update of vignettes during staticdoc documentation
| -rw-r--r-- | vignettes/FOCUS_L.html | 122 | ||||
| -rw-r--r-- | vignettes/FOCUS_Z.pdf | bin | 214499 -> 215014 bytes | |||
| -rw-r--r-- | vignettes/mkin.pdf | bin | 160335 -> 160260 bytes |
3 files changed, 61 insertions, 61 deletions
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index b5228532..a06f360e 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -236,17 +236,17 @@ given in the FOCUS report. </p> summary(m.L1.SFO) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:49 2014 -## Date of summary: Thu Dec 18 09:52:49 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:55 2015 +## Date of summary: Fri May 15 13:29:55 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.214 s +## Fitted with method Port using 37 model solutions performed in 0.082 s ## ## Weighting: none ## @@ -323,13 +323,13 @@ summary(m.L1.SFO) <pre><code class="r">plot(m.L1.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> The residual plot can be easily obtained by</p> <pre><code class="r">mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> <p>For comparison, the FOMC model is fitted as well, and the chi<sup>2</sup> error level is checked.</p> @@ -344,10 +344,10 @@ is checked.</p> <pre><code class="r">summary(m.L1.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:50 2014 -## Date of summary: Thu Dec 18 09:52:50 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:55 2015 +## Date of summary: Fri May 15 13:29:55 2015 ## ## ## Warning: Optimisation by method Port did not converge. @@ -359,7 +359,7 @@ is checked.</p> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 188 model solutions performed in 1.085 s +## Fitted with method Port using 188 model solutions performed in 0.412 s ## ## Weighting: none ## @@ -443,17 +443,17 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) summary(m.L2.SFO) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:51 2014 -## Date of summary: Thu Dec 18 09:52:51 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:56 2015 +## Date of summary: Fri May 15 13:29:56 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 41 model solutions performed in 0.238 s +## Fitted with method Port using 41 model solutions performed in 0.091 s ## ## Weighting: none ## @@ -527,7 +527,7 @@ plot(m.L2.SFO) mkinresplot(m.L2.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAJACAMAAABWh4TIAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+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////isF19AAAACXBIWXMAAAsSAAALEgHS3X78AAAc1klEQVR4nO2dCXwWxfnHF6zaIhECokUELdaAbVDrRQMhEAghcokcRRIVlBKOVjkEESFaFJF/QVArh9A2FkWERvtvFTxQRIN3UJBThHClJNyQkDt5p++RxOR9d5OZ2dl3932f3/fzYXgz7Dzz5P3y7ju7O7OrMUASze4EgD1APFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4opgQf2ItcDAZZVaJX5P8EnAuXfdZJn6xfFtgOaODJf5Q5kn5WEA5QRLvSh0yq9cL8sGAaoIk/h9Pu4uh2+WjAcUESby3m9eXykcDigmS+GlfuIsX18hHA4oJkvjtCUfZtq5n5aNRY9t0hWzV6SBYo/rP7uw+Mls+GDlWPJWljDkv6XQQLPFbhsWn5sgHI8eKV9TFWmWj+F09DrAvY/Plo1EjXMRP/9RdvPBP+WjUCBfxD+xny9maJfLRqBEu4v8+n3Vjd+uNLoEu4SK+cmRyx97z5YORI1zEM7b37v+Xj0WP8BHPVup1DwwII/FbJsrHokcYiS/sIx+LHmEknsXKx6JHbfEVz8X3XFD/FDkDfPrsFt//tHwwctQWP2tOReWzU2SiOEO89+wd4MMt3pW938ftnqLzD1U/VdbaSpsR12WXe9x8XcTPn3X/tLATW9a+VdJ37pfLh189jw3Wbixn9ov/xwr5YORwiz83LtXLmDaest0Dvp/Gnqi1lbaYrYhhpW3nln99sfunpSyzw8HS9Gj3y5Vs10VO+cR/PVk+GDlq7+r7uR0d7q23lXaYnW1Uwip3rB7nNqWdZ3M1N42LmVbodWep+LNTo5o2iXrknH99gPjCpIaDgSpqi98bO2Vq7E69rbTT7LhWxPr3W7nTp3nJBMYqz/i8WS1+QMrH2dmZYwb51wfOq8ewnp86h3NlWV+X6m6lPVX+WGdWqO0pe1kr9cjKbr2nNK33j+K9zSwS37zEU7ra+NcHiu+HqVfc8B3Ha9Nb3ubeFaS1vG72oGSvrIyoyMTDNeL7tbVwcNdzyt7i4n2zevnXB4qf9nnD0YAPTvFcsSwSn5cSoWkRKXn+9YHi0//acDTgIwTEu3fz+fmuWj9+Nc/LXQ/5b/el1EkImoSEeD+yfetw+93p/w8Fd0hEI0oInKv/pN0NuxObdvvev37S8IBNuzUcDfgIAfE3LZl3Yeq3U+L863XE34FhPS8hIP6SslNaLjsf4V+vI/7hLxoOB7yEgPhfrV2urWHv/dq/Xkf83/7ecDjgJQTEvxf5i41XdGnxb/96HfFfTG04HPASAuJZpYudWn8ooFpHfAHO1vOyKjpBGdF6/4msuzqnIx7DeucQXPH3HJSPB5QSXPGL3pCPB5QSXPGfPCYfDygluOJx0tYxBFc85mI4hiCLT8HoziEEWfxCjO4cQpDFf4zRnUMIsviCvvIBgUqCLB6jO6cQbPEY3TmEYIvH6M4hBFs8RncOIdjiMbpzCDzitWqEIuuLZ12FggCr4PzEL7w/79iYdKHIBuJTAqdsABvgFH9NEWOF7XU3MVotayB+4ZtiCQJr4BTf4gxjpy7T3cRotayBeIzunAGn+BEDtm7tf6/uJkarZQ3EF+mu8AfBhlP8yZQWl43Sv3mR0WpZA/GsZ7FQgsAauA/nDHUZrZY1Ev/oJ7y5AQvhFL8tuvGZ3ocNNvJbLfuFb7VsrMFsm3VPi+YILIBTfJ90rTLN+NaUroJaPxzZ4GVwwHDPx7l+YhkCS+AU36pMY6WRuptkTmKfttV+HfAwQaNdPetWLpIgsAbe4/iDGtsVpbtJhwwWnZb7ZBf/ekPxE7NEEgTWwCl+aUdtQmv9C2stSljzXFbSzL/eUHzGQpEEgTXwjuozn1l6QH+Te+/ZNn1OwQsBq6MMxR8fzJ0dsAxO8TOMNyue2U7TfnJHwNMEDcWzWJfRv4CgwSn+wSt6vV5iuFXZqcrASmPxY3Tv0giCCu+uvnx9cjuxe9Eai1+5TCgQsALuM3fHltwWcDq+XozFH04RCgSsgFP8ioTI+z+sEIpsLJ4FHPqBoMMpfsCaItHI9Yi/54BoMKAaTvE3iEeuR/zydPFwQC28U6+WFYpGrkf8obtFgwHVcIpXN9nSS1ecrrebYE+v9jEtUz4uUIL5iRhG1Cd+4wzRaEAxKiZi6FOf+LKAO9+CIKNmIoYe9YlnQzG73mbMT8Qwol7xf8ejpW3G/EQMI+oVn3uXUCygHPMTMYyoVzyLM77WB4KB+YkYRtQvPu19sWhAMbzi3R9QwbP19Yv/fJJYNKAYTvFP3loR22ieUOT6xVfiZjj2wik+cs+6gbtb6W4iuFrWS/mXm0bs5c4RWACn+Mu3Dck41EJ3E8HVsh5y4h5+vONY/iSBejjFL2p0a/kVT+luIrha1sPwbYzlXYopl3bCO7grchmdrRddLeumh6f45Xt8GQJL4L5IU2h04C26WtZNfNnOrJLb8GQiO+G9SHOL1jjeaFO/1bKfTPdym/GTh+a3GT3l2v63Y19vI5zib5516sSj9TxKqPb19aNVq2WNF8zc82RSQlrMHz/jTxOohlN8y3LGyvTvgVNnu1o09B1/35sPNpgdsAxO8Wmz9u+f+aTuJj8xuAlefd/xnoU3A87ejglY9mH+BoebO7yem6vl5vrX1yP+uallbFUKewzjevtQMOcuN36+S2xX71rWM35mIds1iiM6sAYu8YWzfnNJx4cMnwFeNvEuMfHVdBFepQFUwSO+8FeJ63/YOKx9vuFma0YH1nGIX/Bqg5sAi+ARP32Y94h75MNCkTnEnwk43QeCBY/4Tt95X+/oJBSZQzwb85VQSKAOHvFNfGfpi5sIReYRvx3DO7vgER+9w/t6R7RQZB7xLOGYUEygDB7x0+72fsffJzZbikv8mrlCMYEyeMSfv/6Od/d9OOwaw+M5XbjEl8fg7J09cB3Hn59xQ5PrHxTzzieezcZzC+zBntWyP3IMDxa3B7vFs3txg1NbsF383jvluwDy2C6e3fe5fB9AGvvFH8D96+3AfvFs9Cb5ToAsDhB/CJdqbMAB4tm4jfK9AEmcIP4IPvLBxwni2YxaEzLOzUl+/IR8r4AT8+JlVsv6URRT8yzDwriMo+ti9B9tCBRiXrzEatkA3plQ/eqVF93FmkX8TYEc5sVLrJYNZEj1WZynN7iLrRMFmgIpzIuXWC0byJEuVZdn336seHfhwpcFmgIpzIuXWC2rw4LnfX+7Ol+Z0PbXuEhvOSpG9X6rZTf5VsveKnQjzPIeu71/b/ndpsUfjN0g0hTIoEC8y7MsovLHJVQnsryMEHu83L6u3tUV8992F189ItQUSGBe/JarL0wuYwUiiyZ1WTveUy5Z4y4+nC3WFIhjXnzMguNjJykQz8auchdHuh9lJxN3CTYFwpgX36yIVdy0R4H44tjv3eVXfeP74HKd9ZgX334rY+93O2tePNvdLeD0H7AK8+JfazaWsekdFIhnG/vg1sbBQsGo/kime2S/eY5/tYR4tnqEzkNqgRU44urcj/xZbEUukMZh4tnE/5PvEQjgNPGVf5wl3yXgx2niGZs3Ht/zQcB54tmLyWXyvQJOHCievZIYcO80oBonimdbOuPynNU4Ujw7O+wJfNFbizPFM9fcpB9MNAcN4lDxjG2Pf6LUVABQL44Vzyr/0g1X6azDueIZy0ntv8VsDGCAk8UztnfEcF715z7FN4MIzhbP2LaRvd+s4NguvtkvIyar6JAKThfPWN4Tv/1TgyP86TczVtjqAzU9ksD54hkre2t4z8VH6t3kV++4iz/cq6pHHYoPhNdk/1AQ7+bsy0PiHv/SeJ//m9fdxX3jFfbox5+6jYpZY1344BMi4t2UvDux24D5n+tPzlre+hj7ptlOtT3W4o0prLisbzidUwod8R5O/uvhXnFjl20+E/Avj0Y2v/w19R1WM3Z2TFLcY8ut68ByirLrflWFlngPFTtemd6/W+L4Z/+9PXiTcu8aWM7yOz4ftP6UM6lNu7bptSsccWMECYp3/GvBgwO6xyWNnPH8ax9+d9TgwbeqGNLrPMuJesHaTizkpdabT/+zZe2vQmfcGEGeov2b31g8+8ERfbp37x6XOGzMxBnz/vLS629s+CAr64f9R06fVjRf+48vxPfoNztdTTAbuDndXdz9h1o1FtwYYUOql07Gz5a1iuJj+7dkvr/21ZcWzkubPjE1ddiwOxMSusckJMTEJiTE39IjISGhxy3ulz6GDqvL71ON6Rf1+9RRV6bUs4WziUxKXcbGj6z1XllwY4TTvtWyT/1Zzl5wKD5dmwP7G+Lpm+M6v+p9tTcrBLkn6pMjR36+vtYbYN2NEdYsFpXhbCweRVhL6W3tOl5V5/yWBTdGqCLcxIc2ro/St9apsO5wDuIdDcQTxTrx796YUIdLWpgn4lIFQX6qIEYzFb/NzxTEiLw8QY4O/7VKvD89FMRYpuKsrIpEPnhSQZBBgWeehTl0n/kYekC8PhDPD8T7A/HcQLw/EM8PxHMA8fpAPD8JCmKsUDH7SUUiHz2tIMhgBfMJjlj0IHaF4vMVxChRsXJeRSKVhQqCqEhETZBAFIoHoQTEEwXiiQLxRIF4okA8USCeKBBPFIgnijLxeb0iB541GaOLpmkp5kJUdMo1nYwvhrlkVl/bvMs3JhOpiqHgXdFBmfiUySXJU82FcLXMKSgwN8n5uc5artlkfDHMJZPT7Nui+Ve7TCVSFUPBu6KHKvGupnvZpg7mYuQ1vaVp0gFTITa+5ZVmKhlfDHPJZI5l7PiFRaYSqYqh4F3RQ5X4fK2YZUeYi7ElJuvkyO4mE/FIM5uMJ4bpZCrGJZtOxBNDybsSiFLxTczHOdqoyFyAGvEmktFyzSez4aZJZWYT8cYwm4gB6nb1+9jmKHMxvtzE2KkLTV6Y9e3qzSXjiWEuGdej3fYwk4lUxVDyrgSibHCXnOZKnWYuxMetdpRNHmIyD++n1WQynhjmktkcdbagoKDSVCJVMZS8K4EoE58b3+ZOsxNOFlzRcvgJkzG84k0m441hKpk5modcU4lUx1DxrgSCEzhEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKLTF33DBBVrjC660Ow07oC2e+WbU5hJ8Fwj+ynXxiC/9yO4sgg/Ee+bQu98FbXKncRMHX5XG2LL2rZK+szsty4H4avEf5Whvs10XscwOB0vTo+1Oy3Igvlp8BdMqPa/metavNA7pZ5HxAPHV4n3vhcaWTGCsUsFtpx0OxPuLz269pzStt91pWQ7E+4tnGVGRiYftTstyyIunCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiimBB/Yi1wMBllVolfk/wScC5d91kmfrF8W2A5oyGeJhBPFIgnCsQTBeKJAvGhwK7pptiqExLiQ4FVM7NMMOclnZAQHwqs0lNnrrV58WenRjVtEvXIOf96iFeHI8UPSPk4OztzzCD/eohXhyPFNy/xlK42/vUQrw5Hiu85ZW9x8b5ZvfzrIV4djhSflxKhaREpef71MuILPtst3ogAjhTv3s3n57sCayXEvxM7Y/Qd+cLNwh+HivcjI8HLNX1FGxZ1KXLLnyjRZbgTGuKrmDRctMVX0zxlvHyXYYsjxedW4V8vLn7fA+6iMEm0GQEcKT5a8+FfLy7e1fdtdn7kGtFmBKhRd+6R7n0E3iCtbuvamBdfcftbuvXi4tmZh3skrhVuRYAadQPfZIUPrOJuZ6l4tmijbrWEeGBAtbrDKe6iJLGmXpsR12UXYyuvi/j5s+6fFnZiy9q3SvrO/XL58KvnscHajeUsBAZ3wIhqdVu9hzw9auq1xWxFDCttO7f864vdPy1lmR0OlqZHu1+uZLsusvoTbwDEq6NaXWnMecY2Tqip1w6zs41KWOWO1ePcprTzbK5ntNW4mGmFXncQH+LUqHu/68zUPmdq6rXT7LhWxPr3W7nTp3mJ+z9F5RmfN4gPfX5Ud/7THbXOkmpPlT/WmRVqe8pe1ko9srJb7ylN6/2j+NK6rWsB8aGA0XG8Nr3lbTsZS2t53exByV5ZGVGRiYdrxPdri8FdSGMoXr41xIcCEE8UiCeKI8/VGwHx6oB4ooS7+DOTuvfGxTkdwly8q+86Vjjqdfkuw5YwF7/vfndRhIkYgYS5eN/Uqx4NbUaQf12fYILoV3RCOkm8b7LlJPkuAT9OEs/ejZ3xQF9Mrw4KjhLPzmNBRbBwlngQNCCeKBBPFIgnihrxhTp3xIV4R2NefErxgbhGFw496V8P8Y7GvHitIHHU0ZOT7q6pyNngZfBdtbc6MnXY0wVyKQIrUCH+ktOMlbasqfh8npfY2ifdj8RsOr42vlQySaAeBeJzbvmGsR2t/evr7OrTNriLZ/4jmBywDvPiu7Rs2oVtarXQv76O+FEH3EXGX8RyAxaiYFTvytvCvvwkoLqO+Of/6i7GfiqUGrCSIB3Hl9wx49XRltzk5IQVQQkQrBM4rg/+miUfy5BlzVtF9LcgbvgT2mfu9jbLZpU3Tba+o/CDR7ymGdztpF6CIX7mne7im2us7yj8CO1PPMRLIyC+NFkocjDE77v0e1Z+wzTrOwo/OMUvuNi9p7+r3k39Ccq5+hWRl0WIpQV8cIq//OuxO9NnC0UO0kWagPvkq+G/4X6YyCm+afnqlWVRQpFD+erc/vgRQ/uFt3pO8dc+s73/npb1bupPKItP3MvYpyl2Z2EpnOL/0bayv/a4UOQQFn9uoKeM17kld/jAO6ovZaxQLHIIiy/2XlEO7yU9oX0cbxXJ/2ZsSXgfJXKKd+iZO6s4N6FH95nhPW2E/xN/7umXhSKHsngCCOzqXdFCkSHe0QiI/7CFUOSQFv/VzCfDfBWfwHf8TwNmV9VLKIv/27BPPkh8x+4sLAWjeh1cMeWMFeBwTo4QFn9iqKeEeOdOxLAIV0yZ+ygmdB6HtSkxPkl0IivnJ37h/XnHxqQLRfYTv3P9EaHmtrJy0IZ1vT6wOwte9vQ6eeJ4z/1ijTjFX1PEWGF7och1xFfeM3LegGfEUrOTb+fMq/+NcRLzpvx2cMwUsaE3r/gWZxg7dZlQ5DriVzzrLpK/FQoAOBkdX87KYic0vGGdRnziRwzYurX/vbqbnJ0a1bRJ1CMBEyLqiH/AsyNai+dLW8L9fVzM1XO8WCNO8SdTWlw26rTuJgNSPs7OzhwzyL++jvhHN7uL5zLEcgN8zBnfbUzshD+LNTJ/ONe8xFO62tRUbEj10qldauoW9n2qt/xd67u3bL7l/lo1KJWVQ9tt3PPFVb8Ta3X9dg7x2kf1HM71nLK3uHjfrF41FaezvIxIyMo6z0qyfOVrSXF/OJhVuwalsnJR1/huLwi2uusHDvG59V2hzEuJ0LSIlDz/+hA+jqcA767evT8vMtrIlZ+vM0sJ4h0Np/gnb62IbTRPKDLEOxpO8ZF71g3c3UooMsQ7Gt4FFduGZBwidD0+/OEUv6jRreVXPCUUGeIdDe/grsjFisUiQ7yj4T6BI6gd4h0Op/ht0Y3P9D4sFBniHQ2n+D7pWmVaH6HIEO9oOMW3KtNYaaRQZIh3NLwTMQ5qbBedZdIE4BS/tKM2ofUbQpEh3tHwjuozn1l6QCwyxDsaTK8mCo/4shVPfMF2ZTzfWygyxDsaHvGj29/f8aGf9Ro6QygyxDsaHvGR2exzba9oZIh3NDziG7tYhSZ8QxiIdzQ84i9gMqM8iHc0XJ/43Nxczf0nVygyxDsaLJokCo7jiQLxRMEjRomCR4wSxYJHjFYB8Y7GgkeMVgHxjiZIjxgFTsOCR4xmJHhp16ueRsBugvSIUT/KwvpW8CGBmsO51Tp1xuIPDewd91B43xva+agRr7eZsfie2xlbPpMvMrAIG8Tn3OMpw/u+kc5Hjfj5OnWG4g97754F8fZix7n6+F2MvfyofGSgADvEZ/e7o/uEEvnIQAH2XJ0rqZCPC5SAy7JEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USxRfyO51cETMoFwcUO8X8b9ObLMTvkIwMF2CC+tEsFY4cHykcGCrBB/O5xnhLX4+3FBvGFnmd3nkmUjwwUYF4813Pn6jBr4s7Pem/kSA5Yh3nxXM+dq8t/xj+CsZ3NWPDcuSpwHO9ozIsPeO5cFRDvaMyLx3PnQhIVo3o8dy4Ewbl6olgg/njVs2UHy2UEgoJ58blV1FRsmu4lYbTZ3ICFmBcfbXATvDWLpZMC1mNefMXtb+nWQ7yjUfAdv0j/7CvEOxrrRvUQ72isuyMGxDsa626MAPGOBuKJYt0dMSDe0WBwRxSIJwrEEwXiiQLxRIF4okA8UawT/+6NCXW4pIUZmkSaaX1pU1Od/9RUa3OpNzOXerMEAzr81yrx/phbM3XvYTOt/7PAVOfmUh91wEzr9fNMdS6bOsR7gHg7UvAB8XJAvBkg3o4UfEC8HBBvBog3Q4Kp1iNzzLRet8hU5+ZSf+CQmdbv6V3y5kc2dYXi821sXV5kY+fmWlfYk7pC8SCUgHiiQDxRIJ4oEE8UiCcKxBMF4okC8URRJj6vV+TAs9Ktu2ialiLZtqJTron+fa1l+199bfMu30h3XtVasvPXr43o9KF038rEp0wuSZ4q29jVMqegoFiu7XOdtVz5/n2tZfvPafZt0fyrXZKdV7WW7Dz/4vfLX7xK+hdXJd7VdC/b1EG2dV7TW5omHZBru/EtrzrJ/n2tZfvPHMvY8QuLJDuvai3Zef5617H0aOlfXJX4fK2YZUfItt4Sk3VyZHfZ1h518v17Wpvov2JcsonOPa2lO/9Bu2irdN9KxTcxE+FoI9nLVDXipfrXcs30v+GmSWXynXtby3d+7onfSvetble/j22Okm395SbGTl1YJtnat6uX7d/TWrZ/16Pd9jDpzqtaS3ae9SfGTjeR/sWVDe6S01yp02Qbf9xqR9nkIbKtvZ9Z6f49rWX73xx1tqCgoFKy86rWkp3nRH7rWnK79C+uTHxufJs7A55lwM2CK1oOPyHb2Cteun9va8n+53hvAJgr2Xl1a8nOX/tF89id0r84TuAQBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4otAWf8MFF2iNL7jS7jTsgLZ45ptkm0vwXSD4K9fFI770I7uzCD4Q75lW734XtMmdxk0cfFUaY8vat0r6zu60LAfiq8V/lKO9zXZdxDI7HCxNj7Y7LcuB+GrxFUyr9Lya61ne0lhysX7oAPHV4n3vhcaWTGCs8ozdaVkOxPuLz269pzStt91pWQ7E+4tnGVGRiaZunh8SkBdPFYgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEE+V/l/1/knfLCUAAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-9"/> </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 @@ -548,22 +548,22 @@ plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:51 2014 -## Date of summary: Thu Dec 18 09:52:51 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:56 2015 +## Date of summary: Fri May 15 13:29:56 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.464 s +## Fitted with method Port using 81 model solutions performed in 0.192 s ## ## Weighting: none ## @@ -622,7 +622,7 @@ experimental error has to be assumed in order to explain the data.</p> plot(m.L2.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p> <p>Here, the default starting parameters for the DFOP model obviously do not lead to a reasonable solution. Therefore the fit is repeated with different starting @@ -634,15 +634,15 @@ parameters.</p> plot(m.L2.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p> <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:56 2014 -## Date of summary: Thu Dec 18 09:52:56 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:58 2015 +## Date of summary: Fri May 15 13:29:58 2015 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -651,7 +651,7 @@ plot(m.L2.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 1.986 s +## Fitted with method Port using 336 model solutions performed in 0.775 s ## ## Weighting: none ## @@ -723,22 +723,22 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) plot(m.L3.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p> <pre><code class="r">summary(m.L3.SFO) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:56 2014 -## Date of summary: Thu Dec 18 09:52:56 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:58 2015 +## Date of summary: Fri May 15 13:29:58 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 43 model solutions performed in 0.246 s +## Fitted with method Port using 43 model solutions performed in 0.099 s ## ## Weighting: none ## @@ -809,22 +809,22 @@ does not fit very well. </p> plot(m.L3.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15"/> </p> <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:57 2014 -## Date of summary: Thu Dec 18 09:52:57 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:58 2015 +## Date of summary: Fri May 15 13:29:58 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 83 model solutions performed in 0.475 s +## Fitted with method Port using 83 model solutions performed in 0.186 s ## ## Weighting: none ## @@ -882,15 +882,15 @@ considerably:</p> plot(m.L3.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-16"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-16"/> </p> <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:58 2014 -## Date of summary: Thu Dec 18 09:52:58 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:59 2015 +## Date of summary: Fri May 15 13:29:59 2015 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -899,7 +899,7 @@ plot(m.L3.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.813 s +## Fitted with method Port using 137 model solutions performed in 0.314 s ## ## Weighting: none ## @@ -980,22 +980,22 @@ FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) plot(m.L4.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-18"/> </p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAFoCAMAAACMkBkOAAAC+lBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgKCgoLCwsMDAwNDQ0PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+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///83pT05AAAACXBIWXMAAAsSAAALEgHS3X78AAAR+klEQVR4nO2deXgUVbqHS9SZiyDgOI53ruAwI4wKjMN1BrKSHSOLLGICUSJg2AZIgkLIkDDsq2xRwIAQFpXAhMsge8QgBkRgkCTEEHYEJBgJ5CZk7/R5nqnuBEhvSa3dXfX93j+K6sP5zjmp96nqWk59zTFAEs7VAwCuAeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJIkB88XsdWjTvMPn/1R8McB4CxPeJOHTpUmZUP/UHA5yHAPGtKk1L42/VHgpwJgLEB0w8V1FxISFQ/cEA5yFA/M2IlhzXMuKm+oMBzkPQWb2xpMSo9kCAc8HlHFFkXM79vBW4MWnVcsXbXM6dr2t5dGgycF+8LsgVb3M5l1XXcmCvpmOByxghW7yjy7mYMMmDAuojX7yjyzmId2vki3d0OQfxbo0S4u1jKb548YT1NdIbA0rjJPG3PDZnL+tTy6+Vv9vDb1K59GaBMsgXX1CPdbmF+Fk7+EXiPn4xahNj68eIGyRQHvniO3F1WJdbiI+8wpZc3JbEWG2A6aN/rbhRAsWRL97w1512yy3EL0hlB/v/YZGRVYWYPgY3ftsIqI8C3/FLM+wWW4gv9f0wI7HPe54rSvueZizn/m2+vZPmXRcyTKA0zjqrr9wwfZeRlSV7D/eIiAi+Vl86JfrbXZ6npfehX7Z0DVKMrpvtdOAs8fc5+EbQ+nvf8IWv8osrr0vvQ7+s2aRcW58m2yl0unjGrid4LLxlXvsm3rT0k96HftGjeMaqPvEfdoL/91ZPfnH+Del96Bd9iuc5OcJvfQWb8c7BVI986X3oF92KZ+z2Eo/3LmTOWF4ovQsdo2PxjBnTB/b6l0F6D3pG1+J5rk3rNsvmZi/Qv3jGqtNC3/gSk3St0b94nrMTPRbfkt6RLiEhnrGKTYFvZkrvSocQEc9zerzX8tv161fnTNonvWNdYCHekJMl7Ry4Tp97i2esPCVg6GHTSpbXnuMT4qX3rAcair/oO26C9xkprWhDPE/OeM9lRWzAVX419GfpXeuAhuJ7n2Xscoi9Wly8r2ceYxufa/mbxfynJZ3ZR+2ffCWHX10d1m4+G8D9yTTdTQPi+d1+Q1DES6aT/PhvpHetA3jxJVPizExuZ1r+fmLdp7iGp8HcCrbGg1U9M7fmxC/5T6tYZscrVSmd+NWNLO8XGtrjzeR16LrgJgsukt61DuDFG7/7dx1dTYuXj9d9ONnw2567yoofqmS1uZtH86a4u2yuaS5UswrGlZndaUs8O9Mt+q/PRtK+pdfwUB+/oNaYFG2vFnebFXLlrHevjd/XaV45lrHaO3XetCeeFS6fmZbQfdpl6Z1rnobiaxb6+8+tsleLm1Xz926sjMuvXs9VmWRdejq/KjH4gXhzlHbEmzHsGhD6WYW8NrSLsOt4Lu6Jv3zPWOITz83oN8QsK61D65Cr98X3ekYzJ3cWFCz0GHdKdiuaRKB4QW1pTzzP4eG+H9xuupruIC+esdJ1geH7yJ3pQbyJs/Hd/n5OsdY0AZ179Y1j2BMWuLZEwQbdHYi/z+0VPd46QObVK4hvSO6kbglEDvkQb4lhT3hg8h0VGnY3IN6GO8mB4bt1n2NBA+Id5blTMRXK+WndY3V+Y2fNkouKsVQd8Y7SlquaA8d46B3vhXp+z/bkSAX5t50O5It3lLZc7eRHFal9QzeUqtuHjtF0nrvCJP+IPbr/ulcHree5OzvNI/qYMzrSGyrkudtW9zp+Wyf9dIHxyN88p593Tl86QoXLuZrbZsY4L8Fh9c5wv2V4E0sU8sV/3bbLmZAWPmety52b2bJ0Y6/QlGJn9qhx5It/aeX8R0eemuhrXe70lKY/fRDw+jayU3bEIl/8Y9VFXAG729K63BW5bC/P9Y7ch9N8IcgX/8LW1dwWtv9F63IXJTHOndp97NdkHuJJR774/a1/l/GUZ5sd1uWuy159LLb7uydc1blWUOCsvtbIivb8YFPsyrTltV+N6T41x3X9awDtPZ1zwOVDlsl0avYP85qBvEoO0Yl4Y9SgacHLrQqrdr7pMwe3duyjE/Hr5/GLQbYH94rtg33nX3TiQDSDTsSb/4wtK+39V/m28B4L4N4anYiffJRffLDVwf+Wp4X1wH5viU7E5wb+yLK8bWYBPcC038/F9/0DdCKefduvx7ArjVcp3z7Ed1aec4bj/uhFvDAqP4/0Ssxy9SjcAlriear3RXnEHUdGRXLieQwZ4zyiD5F7D9MSiuJ5jEcne0TttZtlgghExZvInu4zeCvZabqExfNcWBjQdy3NhPm0xfPc+OjVoCWXXD0K50NePE/x5jCfad+5ehROBuLNVO4d1X38AUq/fwnx9zAei/cenEpmoi7EN+TCkpBXkpq486sTIN6Kok2DfBII3NmDeFuq0sd1j9pR5uphqAvE2yd7jn+fVdearqdZIN4hP6W87ptwTK9T9CG+MarSJ3gM+2cj8zu0C8Q3xZlFIT0X62+eNsQLoHjr2x7j9+rrfUyIF0bt8el+fVdcdvUwlAPihVO4Mdzr3QOVrh6GMkC8KAxHEnr0W6WHe3sQL5qbG8K9J6Zr/Rsf4qVgODrNv3eSTfYXLaHJlKZuwa3Nb3uO3q7Za3yNpjR1D4xZC4IDZx/X5M097aY0dRPu7hznGbZOe2l1NZ3S1IncXTUl1eGefTl5oFfM7rvOHI9stJ7S1Enc8Vp7dF7/Rp7SG76dGRQ854R2jvoqpDStqs9s+YaccbkZc7fxiynpjVcq+Xy816BkjbyOLUQ8dw+H1YwN30vYMcjMc0GKDNA9iDTds9mW1HTFa+uGeIzceqvpiq5G4B6/ZNjNn6JS7FbJjGFHnuFePG1drqtDvaA9vh5j9uLevpPTy9UdkVwEin+W/zPK2tut0jGNdUosmOlpXa4r8cVeH38zZ6DwmXjVX08LCJl91I2TbAoU3+YOY0W/slulTSVrVcAqH7cu15V4VpYcv1XkmVvJrlifvsty3HTepkDxg/tkZfV+y26Vt97MjptdmuRjXa4v8RIpTB3pGZbsjilYBIq/FdHmV2/b/1XniqltOe6RUJvXzyC+nh/WD/UYut429adrEXw519jTqOoiO0dBiG/AueQwz3c+/dHVw2iAQPHZnZrdCb4qqmWItyL3w4Heo1Pd5Yc0BIrvmcLVJvYU1TLE22LMWd7fe0yqzV1OFyBQ/JPVHKtqLapliLdPbTYvf/TmGy4ehtDr+Cscy+sgqmWId4wxJ2mgd9Qmcd+dyiJQ/Ko/cmOf3iaqZYhvHOP3K8O9Itc92P7VH49f7Ly3tIWe1WfOW3VZXMsQL4BzHw/1HLwy13STx/Bq0ulUjyJn9SxQfHzj1ewB8QK5ummkz2uLj+38B7/+r9nO6lWg+PFPBaaKnFAO8SIo3B773J9nZJRdjnRWj0IP9TV7hrSNFdUyxIvjQPT+xOAXfLc7Kfua4Dt3P638i820ukaBeHEYwxMOLO+R8X4/nxEpTpi4LVD8mqDWw74Ul/0V4kVi3D3zE3OS1TNrIj37Lf5G3YSrAsX32SJ6XgHEy6FgW6xfQPxO9abyCBTfRXzLEC+X8kNz+3iNWJunyhN9oVOvPhKdCwjilcCYt3a4V+/ZBxWfuy1QfBOTLe0B8YpRtDsh2Odvm8TfS3lAToT/BIsHg3hpUiMYTq14y6vvvK+k7fpnffONh7wbxioyEcMuEK88hZ9PDfYZsyFf9Ld+fCa/WNrwYQsmYmgNQ/bqYd69pu+1PxHOAcNNr3lsXdGgBBMxNElx+sze3kM/PCE03/bqpfzizYap2TERQ7tc+DTa3z/mMyHnfIbBI5a8ZvEACBMxtE318Q+H8gf+3T83VTHnc8vMPZiIoQOKv5jzmlfY+4fE/LISJmLohev/F9/Te/jKEwJv8QsVX8mYyLv1EO98jGc/iQnwG7sup+mX9gSKn/mywfuh+aIGAfEuoiZn7Rg//wkb8xp910+g+Nb5u/ueeVJU/xDvSqpPJkf5BsZ+csaRfYHif509MO2HNqK6hniXU3l81TuO7AsUv/Shl2uemiWqV4h3D3j7UT0CojdYpWgRenJXbhR7tx7i3Yiqk2tSLEsEP6QpE5u1GeLdGqEPaf6Xa+Yv7nkwxLs1AsV3TSj6eYpN0gszVHPZahyB4p+o4a8Q7OfAIZzLVssIFJ+YcPHi1Jl2qxDPZatV5Cc4pJHLVncgly1RBIkvS/jzY3+c4PDdbatctt/ON+P9qiIDBOogRHzZCyF7zmcMal/iuF5mg/WrX5gZYHO6B9wIIeLjBpl36Mh3m6hnCQ71bo0Q8Z1zzOu5ne1WecTBmR/EuzVCxDevu0tf0dxulcMdUwsKuAKb/G0Q79YIEd8p17ye28l+nQL/RUYc6rWGEPGTws3f8UNjHFSqju4P8VpDiPi7z4fuu/DloGcd5+LaMsK2DOLdGkHX8XfjuzR/frzIHGwQ79bgbVmiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBNFvnjkstUk8sUjl60mkS8euWw1iXzxyGWrSZDLlihKnNVb5bJNCzLTNkDOuIDKKCDeaPoJylokONQW8sWfbPfokGpWipSm2kK+eI/3C0fFQLzWkC/+8XJmeCkf4jWGfPHtsxhL9ymGeG0hX/xnj49iLK4jxGsLBc7qr2XyZ/aHZ1sXQ7xbg6dzRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiIG05UVRIW37jCzMDBsgdG1ARFdKWZ8aZCRolb2RAVdRLW75lheRBAfVRL205xLs1KqQtrwfi3Rr1Lucg3q2BeKJAPFEgnijqid/3pyALHmsjj/9CvCxaWero+KNa4q3xQ7yG4iGeaDzEE42HeKLxEE80HuKJxisoPgjxGopXUHwJ4jUUr6B4oCUgnigQTxSIJwrEEwXiiQLxRIF4okA8URQTfzOwdd9iibGbf9/K8ztZTRx5RM4QKiKfeP6wjPj9nVp0zZQcb+hcwO4HS2ijLl7sNlRMfERs5ZD3pIVef/xU+aJ2RhlNFHTk5AxhakTlP2Okx9e22VK1oq3U+GXdOJO4+mDxbdTFi96GSok3tjjHvuooLTZzFGOFj5ZLb6LaP5WTM4R2Waal5Hjjs6tvL+giNT5jp0lcfbCENuriRW9DpcSXcBXsUkvJ4YbRQ2Q0EbOogJMxhGourlXnIzL6z+C4R89IjzeJqw+W1Ib5iCF2GyoqvrnU6C9eiqmW3sSW/sb74qXE3+Jm3Uhsa5Qcf/up1BsjQ6T3f198c2lt1IkXuQ2VO9RfYIc7SIyd4pMvp4kRnIlMyfG1D5eyAq5UcvweT8ZOtZA+/rpDvTlYUhvmeLHbULGTuyGJxpGTpIUe7lBcWlpaK6cJ0x4vPT50fsnMF6XH/9Am/e5EH+nx5j22PlhKG6Z40dtQMfEF/r99zSZbijBmm/fYAjlNmMVLjr/o1bJ7joz4HS+2MO1vUuPN4uuDpbRhihe9DXEDhygQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBOFtvguDz/MNXv4v109DFdAWzyrm6JaQHArEPyTLTGJrzro6lE4H4g3TWrntwIX23l09ID/SWTso/ZPvpLj6mGpDsTfE3/wOreL5f2CZXa8UpXSydXDUh2IvyfewLha09pc0yspzSpcPS61gfh74uu2BcdWjmWs9o6rh6U6EG8t/tLT+VWJwa4elupAvLV4ltahdchVVw9LdciLpwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4onyHwbYtllc+Zl6AAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-18"/> </p> <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:58 2014 -## Date of summary: Thu Dec 18 09:52:58 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:59 2015 +## Date of summary: Fri May 15 13:29:59 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.264 s +## Fitted with method Port using 46 model solutions performed in 0.109 s ## ## Weighting: none ## @@ -1055,22 +1055,22 @@ fits very well. </p> plot(m.L4.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-19"/> </p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAFoCAMAAACMkBkOAAAC+lBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgKCgoLCwsMDAwNDQ0PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+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///83pT05AAAACXBIWXMAAAsSAAALEgHS3X78AAAR5ElEQVR4nO2deXhU1d3Hr6jtiyBLfa21gqUVagVqqRSyTQhZMEHZ1wRkh0DZRCEEiMoiCSKE0BcwCZaA2CwGXxeIsgU1RMSChD0giRiWIGvMvs2c5+mdmZBOJpNw15m59/f9/HGZOc/9nXOYz3Nvzrn33N/lGCAJ5+oOANcA8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8UQRIL7o1c6tWnZe8LP6nQHOQ4D4F8O+zM/PmjJI/c4A5yFAfJtK89b0uNpdAc5EgPi+885XVFxY4q9+Z4DzECD+Wlhrjmsddk39zgDnIWhUbyouNqndEeBcMJ0jiozp3I004MakV8sV32g697215vDgeOC+eF2QK77RdC7HWrN//3vHApcxSbb4pqZzc0c2/J5uFNUxoC7yxTc1nbMXvzb4iqieAVWRL76p6Zy9eHbE82OhvQKqo4R4xzQUX7RmdtKdCdPLpNcHFMVJ4m96JB9f96IxvefB8ld8+8wvl14tUAb54gvrsC9vIH65+Swf9Tm7NvDZJMaSpovsJVAc+eK7clbsyxuIH3eR3+xYz1ht5765jPlhhO9q5Iuv/dunDssbiF+Vwm9e3c9YVVBevxhjYPOXjYD6KPA3PjbTYXED8SWG/8uMGm0e+w84WRvjGXi3/LP50ZcFNAEUx1mj+sqtb+y0zPkK/MPCPDzirOf6hXO+2el5Unob+iW1R4Bi9Eh20ICzxNtw6TKrWRdwlv90PYTfXBwmvQ39kviecnW9H++g0AXiLZz1j6lhX0eaP/aR3oZ+0a14Zor3OnyzH//h+xHS29Av+hXP/7UfOG/x5AMpHrnS29AvehbPWEqv6KVx16U3oWP0LZ4VzR54UXoLekbn4hk7bFiNizkO0L14ZtzquVt6I7pF/+IZuzZuDJZo2ENBPGMHDdGV0hvSJTTEM2O8xyc2XwvenP+59IZ1QQPxtSdyaiXVYtXnxuIZuz2nf/01+xyvjG9nR0pvWQ/Yis8zzJztfVZKLRoQz9i5kS/VLdgcUsBvgm9Ib1oH2Ip/4RxjPwQ52ouLNHieYWzbU61/vYb/trYbe6fTI8+f4D8mjOwYw4Zwf65hbi+esd1eqyrM/1ou3kd+Lb1pHcCLL14YYWFBR/P29/Os3yJu2uzFbWCJHqzqiZU1//4l/20Ty+pysWpLV/7jNnbmFxo54pl5atcz3sjYCHOfAm9Jb1oH8OJN3x2x0sO8ee5b65ejtn/tuQJWdF8lM55KDudNcaVspXktVIsKxpVZ3GlFPGMlr/nuZ2c9UjInvim9ZT1ge6qPXGU0rZ/jaC/uNrvOlbMX+m87bdW8cQZ/+NyxetOUeMZuRPQ/ej1uWbb0hnWBrfiat/z8VlY52otbXrOoFyvjcquTuCqzrPzHcquiAv8r3hKlDfH8KG9EWPOdooCweTwX0b7nacai2j+1dFCoRVZ657ZBBfXi+z+hicFdPSdCp1JfiSdQvKC6tCOescy+r9K+XUtVPGN7fSMoj+vpimemD71fvyOvCg1D5Fq9Y0yfGCJuy61Eo5AWz09J07yW3Lz3bjqEuHjzUe83m+LdevLiefU7+86ktzAP4s1khow7o1hl2kAD4pvKc6egeMa+HTb0kILVuT+Ja/MUI1Yd8U2lLVdUPGPnpgR+Sihx6tGpCnLEQQPyxTeVtlxh8YwVRnptxtI8pXBenjsFKInrvZz2whzlcF6eO0WoTfWbclqFeumhQp67HdbH8Tuo9OqCb0b134UUOrJRYTpXc9vCdDWOeAuXIj3WF6tVORXki/+qQ/ezQa18ztmXq3Kqr6M80TBT0npjcBf54p/dGPPg1GPzDPblaornyRoV8mGNqi3oG/niH6q+xRWy0tb25SqLZ+zqG72WUryMrwzyxf8pLYFLZbufsS9XXTw/mtgRPHwPBnqSkC9+d9vfZT7q2a5RZmoniOf5foFHDF6AJQEFRvVGE7uV8WOjYueIZ6wqJXj4ZzjsxaKJu3P3Im9R79ezvqS9OlMsuhDPWHXQ43/o9rYTG9Q8OhGfFM0K32o/4rATm9Q4OhFv+W+kLgj3WkX9SQyh6ET8AvMyjX+ksYq0ASHbS53YsGbRifhT/ldYjrdlFdC1WN+xu6VlDqGETsSzbwb5TqhfknlmUa85+HPfPHoRb4cpa3rv16gt0BSFTsXzVO8c47OK3rpsoehXPE952jC/WAzzHaJr8TzF7w8OiIP7xuhdPM/P2wcFrCtwdS/cDQLieX7+1zC/t5BfxRYa4nnKPgg1vHHc1b1wH8iI56nKmNx73le4tmOBknie2q/meUz8CO+0JifezMk3+wxKbPQOZGoQFM9zNWFgn+XfEXoGszE0xfOU7wz3mPox3Rt5ZMWbyVnpHxJ33tW9cA2kxfPcSn7Jc1ZGuau74Xyoi+cx/ntZ35C11G7lQbyF22kTe09KpZRKE+LrObUmxHfxFw7Tg+sQiLelcl+E4YU1xynM8yDenptp0zxHJea5uhtqA/GOuPjPMM8J7+n6UVyIb4ozG4Z5TUvW7aVdiG8G04m4wd7hKbqUD/H3wHg8boj35G2NnwbWOBpJaepaTKc3jfYam6CrrDuaSWnqcvKSJvoMXnu42tX9UAgNpTR1A67tmOsbsCRDD29M0VRKUxdSumlhijXtRmnm8v5eU5MapXfTGBpLaeoq7ni9eyh6cP0VPePJ+PFeA1Ye0PDtfBVSmlbVZbYcIadfbsbKHfxm4Z4GZTc+XRzoM2NrrjYv8AoRz92lyd1MJTZfPh5u4akARTroHowzP4S3Y32j8trj8RO8Q17fpb03Jgk84tdOuPbTlC0Od8may7Kf4J45aV+uq1O9oyO+np/3rhjgPTo2W1PLOQSKf5L/T5V1crhLl3TWNapwmad9ua7EF3lt/vrNoc2d1H9Mnx/gMyXxmFbSrAoU346fwdz6lcNd2lWyNoWs8mH7cl2JZ2XxkWn3TKZnPJ00089vZtJJDTy0IVD86Bdzcl4Y63CXsWOOR6woWe9jX64v8cKpObY53Ndv9tZT7m1foPibYe1+Nd7x6z4rFnfguAeC8+3LqYq3UH00gbf/93ePue2FPsHTuYpm9qq+5eAsSFq8heqcf87q6zt5Q7Y7TvcFij/etcWdQHHPmEO8BWNucsTzPiOjP3Ozm7sCxffbwhmj+omqGeJtuLJr5Qif5xe8f9JtBv0CxT9SzbGqtqJqhnh7Sg+9M93Pd/yave6QblnoPP4ix850FlUzxDvElPf/S4d5Bc3bcsS113sEit/0R27GYztE1QzxzVB6OHFOkNfQ1z/I/e+kr3rzrDVFTuuB0FF9VvSmH8TVDPH35Kd96yYbDGHRn+SZGKsNWX8yxcNpD/MIFB8pPnMQxAvkh12rXjIYxk0MzTexj1Y4q1WB4mc96p8i8oW+EC8GY97UMWMNhsE9PjrvnCt+Qk/1NRmhHV4WVTPEi2PffH7kFzdp9SRfw4jXUo6pPfQTfOXup409Gy2raxaIF4dp1JJ9cX0sF/mu7t84M8gQ/EqCiu/ZESg+MaDthP3izkEQLxLTrmXbbZ/VLf5226KhPr7jo3ecVuERXoHiX0wVfeqBeEWovZCxNjzQEPzyxn0FSi7yEii+u/iaIV5Jio8kLw01GIYt2pKtzOlf6NKrd0QnBYR4Ffjpq3cXDjUYRke9d0jmMj+B4u+x2NIREK8elw4kLBhi9r81W+D7VU+E+c1ucH8QD01qmMtfbF443GAYuiB+X37zI+9zhlzTl9626wIUWYjhEIh3FtcPbV82rq+h34y3P8wpdrxLZBa/ibW92YKFGLqh/NQnsbP6G3xHL0rcn29333+iObVL2gabEizE0B1XDm5bOt7fEDhpxfbsK3UzwIRYfjPmO5u9sBBDr1Sd3xMfObqPb79p0cmHLo2etHZggxtAWIihdyrOfrYpYmSfniMbvooNCzGIgoUYRBEqvpIfNYqrGeLdGoHilz1X631fjKiaId6tESi+be6uAWcfEVUzxLs1AsX/7/Gh6T+2E1UzxLs1AsXH3vdczaPLRdUM8W6N0MFduUns1XqId2sE36QpE7nIFuLdG6E3af7KtfATt7Ye4t0ageJ7LLl1Y2GjpBcWCOSy1SMCxbevYazacQ4cKrlsdYZA8VFL8vIWL3O4C6VctjpCfoJDGrlsdQdy2RJFkPiyJX956I+zm3x22y6X7TcxFrxDFOkgUAch4sv+FJTxfebwTk0s5DOTZfO5YK+FIY2Ge8CNECI+YrjlgB73yj32awhO9W6NEPHdTlg+n+rmcJcHmhj5QbxbI0R8S+tV+oqWDnc52CWlsJArbJTGDeLdGiHiu56yfD7V1fE+hX6rTTjVaw0h4uePsvyNf2luEztVzxkM8VpDiPjSp4M/v7B/+JNN5+JKndS4DOLdGkHz+NLI7i2fniUyBxvEuzV4WpYoEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USRLx65bDWJfPHIZatJ5ItHLltNIl88ctlqEuSyJYoSo3q7XLbpARY69JXTL6AyCog3mV9BaUSCQ20hX/zRjg+GVrMSpDTVFvLFe7x9fdpciNca8sU/XM5qn82FeI0hX3ynHMb2+BRBvLaQL/5fD09jLKILxGsLBUb1l7L4kf3BFfbFEO/W4O4cUSCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oSFtOFBXSll/da2HIELl9AyqiQtryrAgLAdPk9Qyoinppy1M3SO4UUB/10pZDvFujQtryOiDerVFvOgfxbg3EEwXiiQLxRFFP/Od/DmjAQ+3k8T+Il0Wbhjq6XFFLvD19EK+heIgnGg/xROMhnmg8xBONh3ii8QqKD0C8huIVFF+MeA3FKygeaAmIJwrEEwXiiQLxRIF4okA8USCeKBBPFMXEX/NvO6BIYmzy79t4fieriuwH5HShYlz7pw/KiN/dtVWPLMnxtd0KWX2whDqs8WJ/Q8XEh71cGfqqtNDLDx8rX93RJKOKwi6cnC4sDqv8YK70eGO71KoNHaTGr+vFmcXVBYuvwxov+jdUSryp1Xn2RRdpsVnTGLv+YLn0Kqr9Ujg5XeiYY95Kjjc9mXB7VXep8ZmfmsXVBUuowxov+jdUSnwxV8HyW0sOrw0PlVHF3NWFnIwuVHMRbbply2g/k+MePCs93iyuLlhSHZYzhtjfUFHxLaVG7312brX0KlIHm+rFS4m/yS2/GtXBJDn+9qMpV6cGSW+/XnxLaXVYxYv8DZU71V9gBztLjF3okyunikmcmSzJ8cb7S1ghVyI5PsOTsWOtpPffeqq3BEuqwxIv9jdUbHAXGmWaOl9a6MHORSUlJUY5VZiPeOnxwTHFy56RHv9juz2l83ykx1uO2LpgKXWY40X/hoqJL/R7fGCjbCnCWGE5YgvlVGERLzk+z6t17xMy4j9+ppX5eJMabxFfFyylDnO86N8QF3CIAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8UWiL737//VyL+3/j6m64AtrimXWJaiHBX4Hgf7khZvFVB1zdC+cD8eZF7fyvwL3cLXzOkN9GMfZOp0eeP+HqbqkOxN8Vf+Ayt5Od+QXL6nKxaktXV3dLdSD+rvhaxhnNn1aaH0lpUeHqfqkNxN8Vb/0tOLZxBmPGO67ulupAvL34/Mdyq6ICXd0t1YF4e/EsvXPboAJXd0t1yIunCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiifIf38G1fhJa6MgAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-19"/> </p> <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 -## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:59 2014 -## Date of summary: Thu Dec 18 09:52:59 2014 +<pre><code>## mkin version: 0.9.35 +## R version: 3.2.0 +## Date of fit: Fri May 15 13:29:59 2015 +## Date of summary: Fri May 15 13:29:59 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.375 s +## Fitted with method Port using 66 model solutions performed in 0.146 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf Binary files differindex f8fd354f..3174a23a 100644 --- a/vignettes/FOCUS_Z.pdf +++ b/vignettes/FOCUS_Z.pdf diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf Binary files differindex f83f8735..8f1e0884 100644 --- a/vignettes/mkin.pdf +++ b/vignettes/mkin.pdf |