diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2016-06-28 08:23:38 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2016-06-28 08:23:38 +0200 |
| commit | 7faf98ac5475bb2041d7e434478c58c2f2cec0fd (patch) | |
| tree | 837a519b7fe4ad085a412cbb2e61d64605d8cfca /vignettes/FOCUS_D.html | |
| parent | cb338bea13b3b834bc3b09e6b1014959195f37bb (diff) | |
Static documentation rebuilt by staticdocs::build_site()
Diffstat (limited to 'vignettes/FOCUS_D.html')
| -rw-r--r-- | vignettes/FOCUS_D.html | 17 |
1 files changed, 6 insertions, 11 deletions
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index f3eb6a0c..c7e2047f 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -124,13 +124,8 @@ $(document).ready(function () { <p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> -<pre class="r"><code>library("mkin")</code></pre> -<pre><code>## Loading required package: minpack.lm</code></pre> -<pre><code>## Loading required package: rootSolve</code></pre> -<pre><code>## Loading required package: inline</code></pre> -<pre><code>## Loading required package: methods</code></pre> -<pre><code>## Loading required package: parallel</code></pre> -<pre class="r"><code>print(FOCUS_2006_D)</code></pre> +<pre class="r"><code>library("mkin") +print(FOCUS_2006_D)</code></pre> <pre><code>## name time value ## 1 parent 0 99.46 ## 2 parent 0 102.04 @@ -195,10 +190,10 @@ $(document).ready(function () { <p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAGACAMAAABFpiBcAAAC4lBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NVVVVWVlZXV1dYWFhZWVlaWlpcXFxdXV1eXl5fX19gYGBhYWFiYmJkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t9fX1+fn5/f3+AgICCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKUlJSVlZWWlpaXl5eYmJiZmZmbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///97HrBkAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAUIElEQVR4nO3df1xVdZ7H8S+K8uuCOnoLf7b+aHBWRRHB1FXLGjN/ZK2rW8msOrvNpjllgxNTq+2AO5Xjj1lkCgILxcRwG39Mo4Szmr82kbiuQutorokhIW4Jwf3+v99z7gXOvZx776HzPYcPPN7PPw73nss9n3Ovr7jcL5iMAxDGDH5e/XM/BZBmTb3kQMuH7gCQZmi57EATDX4igAGJCBQoQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGuVAmUt7rTglbuYn0o4N3US3CfRI+PritJgqaQcHW528EPJTDtzU29ttAl20lnP33JelHRxstXaz9lpFkWqPT5JzD+nd0f5AXezkjNgJO8WlTx8dGDG+SDn20QdTecuOxKjRm93KtfnxI/L4VcbYq233ckecEtu8yQbPAojxDXTO7CWKhNe1O+kEmlBaWxhbwlsGj83+YGX4bXHsCblVPMuReXiL8w1xbUqle1uvuubrrKyh7V63mPJfW9kQg2cBxPgG+tAR9cNL/6bdSSfQErHNmsW/ThdfFG8rr+Msh/MWR7bYvX+auJbPeaO6W/MSX8WalIP3MXgWQEy3CrRObI8PEptLezJS1BLPcn6ZqZzi2jnuiVMbaB37SmzL7jV4FkDMgklLNO7xvsQnanfG5+ndsSsCVV6sS/tx/mL8itzy1hJrWdF1hbdL/0DdkSJiXpBk8CyAmJVpRRrjX1E/LHpau3PSfr07dkWguWK7ejqvDbumfOFsLXF4utiUZAQIlC9aLzaL0w2eBRDTrV7inZv2r2bF/E7f107sntRr+7eeEt8KzziQ2f89TaC9s6+13+1oxJvH1jmqDZ4FEOMX6M5qxSqigZ6e4RhXIC7tGhk7+/j6uMueJN35E6MScrgm0DXRmzT3K06OnXna4EkANb6B/tMoj0LtTjqBGjwS9CDp/x7yUx7/WG8vAgU7NDWH/JS7untpB1rIWtUZvxP0JJR/Fg+AQIE2BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkSQ+0MipZx5g+0XYJj7JrUqR9D6q3bZMi+to2KlyvFH9RlZID5eVndGxNLLDL8F/bNekf/8auSQVx2+2atGy+XZPyw/RK8feZ0e7M/eMxh+aauntnTDhv16R3lts1id9zw65Jm35h1yR3mNTDIdAOEKgpCNRqCNQUBGo1BGoKArUaAjUFgVoNgZqCQK2GQE1BoFZDoKaQCtS1UdJphPbCl3ZNOr7Nrkl85Td2TfrwPbsm8aelHg3/yjuQhkCBNAQKpCFQIA2BAmkIFEhDoEAaAgXSECiQhkCBNAQKpCFQIA2BAmmmAi1OiZv5iawzCcSdnxh9/6Zme8ZVx7m4LZPO/bjffa+7bRj15U/iBz5zk1s/KVV95tqnSBpnJtAj4euL02KqzJ9EUHvZmoOb+2XaMq4xhSlPs/WTLvT7WfErfd62flTLA6Pe2ZM8j1s9qWmr+sy1T5E1zkygi9aKr29zXzZ9DsHNUn4pOneULePWTVSfZusnPfus2OT/1vpRLnaI8yr2tcWTsiOYJ9C2KbLGmQjUHXFKbPMmmz6H4GbvEJtShx3jDjguKE+zDZMGnvF8tHyUi/0n5zdYrcWTaioq1EDbpkgbZyLQW0z51qZsiOlzCK152QIbxl1z5nPlabZ+0v+xvQ84fvhmi/Wj3HOmnf7siUdteFBqoG1TpI0zEWgVaxLb8j6mzyGkvyyMr7Z+XPNDaZ6n2foH9jn7YUnV+4O32DDqv6MZ63XRhgelBto2Rdo4E4HWsa/Etuxe0+cQQtPGyGU3bBiXldDgeZqtf2A32EmxzU+wftTJuF/duJk21YYHpQbaNkXaODPfg0aeFduCJNPnEFzD9Pv+bMu4x5kq2YYH1ty7XmxPxVg/atVYsbmpfA9q9STP96CtU6SNM/Uufr3YLE43fQ7BrRpba8+4W9cFVnbTjgf24C6xyUq2ftQvhzRyfpHVWz/J+y6+dYqscWYCPRrx5rF1jmrT5xDU3cgncxQ2jVOfZusnfRD7r/+R0fd960e5Yh7eU5SkrINaPckTaNsUWePM/SQpOXbmadOnENwFzwsvs2mc52m2flJBUkzSHjtGnXls0ICnamyYxLw/SWqdImkcfhYPpCFQIA2BAmkIFEhDoEAaAgXSECiQZjTQ+ud+CiDNmnrJgZYP3QEgzVB7/jHZz6z+dXrooWz6145t/H/UQ4+CQIE0BAqk2RTokfkG7w7gw6ZAW24ZvDuAD5sCBfh+ECiQZlOgWAeF7wfv4oE0BAqkIVAgDeugQBrWQYE0LDMBaQgUSMM6KJCGd/FAGgIF0hAokIZ1UCAN66BAGpaZgDQECqRhHRRIw7t4IA2BAmkIFEjDOiiQhnVQIA3LTEAaAgXS7F8HdVn4PxO38tjQJex/F28iIu8/ohd4JwLtcbp/oEu/aL+MQI35+FpXn4GOw1/p7e3+gWohUGOWFZo8QPVHHjeknI7H3EN6e+1fB1Uiujjsme80O07OiJ2wU1z69NGBEeOLlGFHH0zlLTsSo0ZvdivX5sePyONXGWOvth+yYt6AuEdcnmq9n9Hx2KDLdKBPTnxYMfrV0J9qWJcGqlkHFRFVxq9s4ZodCaW1hbElvGXw2OwPVobfFsMm5FbxLEfm4S3ON8S1KZXubb3qmq+zsoa2ezUPeWpX4bxUb6Cez+h4bNBlOtDF+9QPG35l/lzadGmgGi523rm8xWdHidhmzeJfp5/i/LYaXI5o2pEtdu+fJq7lc97IXL4v8VfZJc5r3vYG6v2MDscGXQg0CBcb6JzmG2id2B4fJDaX9mSkqMGd5fwyUznFtXPc26Em0Jal/dPy6ltv8H5Gh2ODrqkPLDFn6HT1w7i/Nnkcrfh39E61K9ZBt58I26a5zcVuim1pP85fjF+RW95aYi0ruq7wdukfKOefb3xkwG80N6iB+h0bdD32fJE5KS+pH5b+rcnjaE3ar3eqXfMu/nnHFa7ZkSu2q6fz2rBryhfO1uCGp4tNSUaAQG+tFW+fPnT4B+p3bNCFl3h/foE2jFjo1uxwbtq/mhXzO31fO7F7Uq/t33qCeys840Bm//c0gfbObl+/ax60oqhofmrHQH2ODbrMB7rzluLlnhooP8h2a3acnuEYVyAu7RoZO/v4+rjLnuDc+ROjEnK4JtA10ZvaD3lsakz/BVUdA/U5NugyHehzAzx+J+V0PKisg3aA1XXbLS/u6jPQsbBUby+B3wdFoLZroLjScVt3b5f9ul0ha3WiE4G236vO+J2gG8PvgwJp+HvxQBr+VieQhkCBNAQKpBFYBwUIjMA6KEBgWGYC0hAokIZ1UCAN7+KBNAQKpCFQIA3roEAa1kGBNCwzAWkIFEjDOiiQhnfxQBoCBdIQKJCGdVAgDeugQBqWmYA0BAqkYR0USMO7eCANgQJpCBRIwzookIZ1UCANy0xAGgIF0rAOCqThXTyQhkCBNAQKpGEdFEjDOiiQhmUmIA2BAmlYBwXS8C4eSEOgQBoCBdKwDgqkYR0USMMyE5CGQIE0rIMCaXgXD6TZH6jL6JEAulmgzBXkxtRgN0J3Zf86qDWBNm0NWi/IUeCWfUR3fvDb7V8HtSTQ7AiGQG0Qezv055wpUr1fb+iId2KC327/MpMS6MVhz3yn2XFyRuyEneLSp48OjBhfpAw7+mAqb9mRGDV6s1u5Nj9+RB6/yhh7VXNC++bcM2Z3QeKAkfs4r6moQKA2MBLohB8vUfxViC+NXiQDrYxf2aLdkVBaWxhbwlsGj83+YGW4eA7YhNwqnuXIPLzF+Ya4NqXSva1XXfN1VtagOaFpV5rXsXk1zT+P91xHoNYzEuj4CvVDWp6hIxIJVLMO6mLnncs1fYodJWKbNYt/nX6K89tKaCxHfFfgyBa7908T18R/i43qbm2D7E9iGLuibDzXEaj1emygPu/iBzqn+QZaJ7bHB4nNpT0ZKWqJZzm/zFROce0c9/TnG6jL+/2sC4Hapu8TS0Lq532JTwn9qcITfYJP7IpAt58I26a5zcVuim1pP85fjF+RW95aYi0ruq7wpodAKYh+pyik4W+oH2b/c+hPFQoig0/smnXQ5x1XuGZHrtiuns5rw64pXzhbSxyeLjYlGQiUkB77Eu+3DtowYqFbs8O5af9qVszv9H3txO5JvbZ/64ntrfCMA5n939ME2jv7muaEEKj9DAV6sFrxZLcK1H8d9CDbrdlxeoZjXIG4tGtk7Ozj6+Mue2Jz50+MSsjhmkDXRG/SnBACtZ+RQJeM8vijoSMSCTQY/HC++xj8jewj3r03+O0IFDrhru2H7LLfBy1krU50ItD2e9UZvxN0Y/h9UCANgQJpCBRIw9+LB9Lw9+KBNALLTACBIVAgDX8vHkjDu3ggDYECaQgUSMM6KJCGdVAgDctMQBoCBdKwDgqk4V08kIZAgTQECqRhHRRIwzookIZlJiANgQJpWAcF0vAuHkhDoEAaAgXSsA4KpGEdFEjDMhOQhkCBNKyDAml4Fw+kIVAgDYECaVgHBdKwDgqkYZkJSEOgQBrWQYE0vIsH0hAokIZAgTSsgwJpWAcF0rDMBKQhUCAN66BAGt7FA2kIFEhDoEAa1kGh01xGY5BwEKyDQqeFbivVpbubaXbTCtSg/zka5MZD10wcGSQK1VbTVqYf6NIvjB/Ei1agBU97L1QVeVzS3Lh4n4kjg0Qh2sqOYAEC7cRBWtFaB20L9GeTlyimrNLciECpUNq6OOyZ79r3sH1z7hmzuyBxwEjxh1RTUeEXaMW8AXGPuDwv8ezo/PgReXoH0UXrXXxboM/+Tv2Qs1JzIwKlQrRVGb+yRbOHTbvSvI7Nq2n+ebznuk+gzUOe2lU4L9Ub6JRK97ZedToH0YVAodNc7LxzuU9a7E/iT55dUTae6z6BXmXiW7Wat72B5nPeKC50PIguWoG+Nvhhj2Fj1Q8/GvpwO+c2g6cAFnOxgc5pvoG6vN9UuvQCbVnaPy2v3rubnfNc6HgQXbTWQX8/5yOPhWvUDy/M+6jdjCKDpwAWc7HtJ8J8vlwED5Tzzzc+MuA33kBd3Buo/0F00VoHxUt8t6BU+LzjimZP8EBvrXVz/qHDP1D/g+giusz07IZqRSYCpUhpq2HEQnf7nuCBNg9aUVQ0P7VjoL4H0UU00NdHeWRqbkSgVKgVHmS72/eEeIk/NjWm/4KqjoH6HkQXrXXQvauC3Pj3hw2eAvQgtN7Fu5uC3Nho8AygJ6EVKHQnhaxV3fe42SAECqTRWgcF8ENrHRTAD61lJgA/CBRIo7UOCuAH7+KBNAQKpCFQIA3roEAa1kGBNCwzAWkIFEjDOiiQhnfxQBoCBdIQKJCGdVAgDeugQBqWmYA0BAqkYR0USMO7eCANgQJpCBRIwzookIZ1UCANy0xAGgIF0rAOCqThXTyQhkCBNAQKpEkPtDIqWcfQsGiLhUdZPCCyj8UDesJD6PMjvT99M6IqJQfKy8/o2DKuwGKj/8XiAesmWjygYOQGiwe8kGTxgIKR7+r96ZvxmdHuDAeqy/qX+KmfWDzgD49ZPIBPOW3xgP0LLR7Ak/7L6gkBIVAEGhoCDQiBhoZAA0KgBiBQMxAoAg0NgQaEQENDoAEhUAMQqBkIFIGG1m0DdW2UdBoB/fKKxQMqf23xAJ7+hcUDzmdZPIC/9L9WTwjIXKAAFkOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIG0zgdanBI385MOV3z2mqM/gPNUl5zjB5jgzk+Mvn9Ts3UDqh93/uDvZP1aUKAnqTpO0rOk/xCYIl3OBGM6HeiR8PXFaTFVfld89pqjP4A3bWWyAtWfsJetObi5X6ZlAxrvT9pTnDxHxvEDPkm8MUXSs6Q/4DDLzsnJOSllgkGdDnTRWvHFZu7Lfld89pqjPyA7gkkLVH/CLOWXr3NHWTbgj+wi5xdYvYwBAR4C5+smSnqW9AdsHyrl4J3R2UDdEafENm+y7xWfveboD+A1FRWyAg0wYfYOcaHUYdmAQ0vcnNezryQMCPQQ+AHHBTnPUoABL8zm9W4Zxzeus4HeYjfFtmyI7xWfveboD1BPVVKggSfw5mULrBzw5ZnlD0k4fsAJ15z5kp6lAAMWjRnB4n7RKGOCUZ0NtIo1iW15H98rPnvN0R+gnqqkQANP+MvC+GorB8xhvSskHD/QhOaH0mQ9SwEewuSJJxuODN8gY4JRnQ20Tn2NKrvX94rPXnP0B6inKinQQBOaNkYuu2HlAM5r10j5HjfAhKyEBlnPUuCHwPm7Y2RMMKrT34NGnhXbgiTfKz57zdEfoJ6qrO9B9Sc0TL/vz1YO+PgwV74H/dK6CY+ri0As2bIBHqdjJAwwrPPv4teLzeJ0vys+e83RH8DlBRpgwqqxtZKOrz/glWHie7fKsDvWTbh1XWBlNy0bUBPxrriwJUXGAKM6HejRiDePrXOIb9WWfqG50nbBPP0BXGKguhPuRj6Zo7BswIXIBXt3T1ws4/hBniRZz5L+gJ/0++0fNkQXS5lg0Pf4SVJy7Ezlf0WgPhOtV9ovmKc/QGKguhMueF4f5fzoV/chlM6Ic/6DrK/SgZ4kac+S7oBvXhwWlbRHzgCD8LN4IA2BAmkIFEhDoEAaAgXSECiQ9v8VYkypuE0lagAAAABJRU5ErkJggg==" alt /><!-- --></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version: 0.9.43 +<pre><code>## mkin version: 0.9.43.9000 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 07:48:50 2016 -## Date of summary: Tue Jun 28 07:48:50 2016 +## Date of fit: Tue Jun 28 08:19:31 2016 +## Date of summary: Tue Jun 28 08:19:31 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent @@ -206,7 +201,7 @@ $(document).ready(function () { ## ## Model predictions using solution type deSolve ## -## Fitted with method Port using 153 model solutions performed in 1.659 s +## Fitted with method Port using 153 model solutions performed in 1.706 s ## ## Weighting: none ## |