diff options
Diffstat (limited to 'vignettes/FOCUS_D.html')
| -rw-r--r-- | vignettes/FOCUS_D.html | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 6573cc7a..9522e881 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -276,13 +276,13 @@ kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If -the <code>ccSolve</code> package is installed and functional, the differential equation model +a compiler (g++) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <pre><code class="r">SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")) </code></pre> -<pre><code>## Compiling differential equation model from auto-generated C code... +<pre><code>## Compiling differential equation model from auto-generated C++ code... </code></pre> <pre><code class="r">print(SFO_SFO$diffs) @@ -312,7 +312,7 @@ using the <code>plot</code> method for <code>mkinfit</code> objects.</p> <pre><code class="r">mkinparplot(fit) </code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAACnVBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcKCgoMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUXFxcYGBgZGRkbGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkrKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVXV1dYWFhaWlpbW1tcXFxdXV1fX19gYGBiYmJjY2NlZWVmZmZnZ2doaGhpaWlqampsbGxtbW1vb29wcHBxcXFycnJ0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6QkJCUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJyfn5+goKChoaGioqKlpaWmpqanp6eoqKipqamqqqqrq6usrKyxsbGzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHDw8PExMTFxcXIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////8uS4RAAAACXBIWXMAAAsSAAALEgHS3X78AAAQC0lEQVR4nO3d/X9T5QEF8AcnL+0ECgUpVVgbkUywIsJEQQEZVUFQVjY3VplTUIZi5wQrkwHyMhDseJkMRGc3YIU5UYRBqUoLayFQtZa2tE3T5Plbdm9eb5P0NvfmSWh7zveHNrlJzg2f09SzLlAhCZIwvfWyYzb1UY7LSRR/YZnlryTqJZZdML2ZxfdXLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB7UTSo+knv10WFPNtqNoa5az3Z3i++zqAM3vfilK9uLXrYbQ13997fhi/vW6y6GrrYURt01RcWLVx+aXillWV7mqI3atU1OuX3cyHlntIs7luSul4vEPZ7AHX0ZX8ljDtOzUMIMxT9Yrnlxb+hq2orfIndOk+6cUs+ng7Vr22SF45J710TtYpmsHGTIbRJtsibT9CyUMEPxM/UP29JffK1sHNAuvWf3LtciRIssFZpb2qS44Q/tWvwQ07NQwk6MKgjJ1D+MvTN0dfKEqLumqvgGWS9a5eOFZecCNW99Xkrv94FAY/G+jAvyeH7PfyRKRC94xa/zrJkqb4iqjt3CrafUjK5yl8yJFO8O3bOoxFe8yvQslDBD8Q83aDamv/jVWVPOSVmSlffGwiJ/yoH8YXNrw8UX5gTHnXTNun3BddOzUMIMxb/+jC78P+LSVrzp4yhFGj7s7pbO/VEHblbx5cKvoKf7UYrwFQ+KP6sHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQLB4UiwfF4kGxeFAsHhSLB8XiQbF4UCweFIsHlVTxNY7iru6aoNbo3p2Xn6M2b9wdavPGLizulqMmieLlxequHvzomFKT1MaVT1Gb9+7TavN+/5LavF/+pbpbF82btfh74AtbrN2/JzPVxrU/pjbv1Aq1eXu3qc17/Zjth7J4Myw+hMUnhcWHsPiksPggFp8wFm+GxYectn2iPpnX9qXavPrLavOqm20/1GLx1F+weFAsHhSLB8XiQbF4UCweFIsHxeJBsXhQloq/+uiwJxtVnXnv+KHTP1eZeeJWqTCu7dmsCccV5v19Ysa9FaryOp0uGc6yF2mp+KUr24tetnyK+OpuO9W6IdenLtPl0P8syuJeW9r+/gp1ed7h+91bxirK2zxV6MUHs+xFWinel/GVPOawfIr4Kp6Tsn5gq7LMjln7hMqnmPuF/lFZnu+OHQ1//LGivCMH9eKDWTYjrRTfJNpkTablU3Src3mRuswVG1xC4VPsEKuHOk8o/CMfEWLgeWV5evHBLJuRlosfYvkU3SmftKJDWeb+p3zh4lU8xW/FuislY33K8hqy910pnqvs+YWLH2I30tq3+gvyeL7lU3QT9sqMKoWZy4SuQtlT9P6gWbpEs7K8w9OlPJWh7I8b+Fbvz7IZaWncFZX4ildZPkV8x/Mbm5ubvQoz9Ve8urifrm9ae7e6vP8N/7jlpRnK8vzjLphlL9JS8a5Zty+4bvkU8b3pf4m6FGb6i1cWV/2TzAfOKMz7290Z+rc4RXn+4oNZ9iL5AxxQLB4UiwfF4kGxeFDmxV92zKY+ymH+Hn6L/yLG7s5EvpqoF1D7T6Go/itUlDIsHpTa4p9oTfLpULqoLb4pyWdDacN/7gyU2uK56vsMjjtQLB4UVz0ornpQXPWguOpBcdyBYvGguOpBcdWD4qoHxVUPiuMOFIsHxVUPiqseFFc9KK56UBx3oFg8KK56UFz1oLjqQXHVg+K4A8XiQXHVg+KqB8VVD4qrHhTHHSgWD4qrHhRXPSiuelBc9aA47kCxeFBc9aC46kFx1YPiqgfFcQeKxYPiqgeVolWv6LdX8ZdgpUyKVr2lxqLu7BLd3dKffHmTz5eiVZ9M8e6jCZ6+T3vEa/EB7/1Zs7Pd5ulaCqMOpGjcCXlt0q5wyKsPTa+Usiwvc9RG7domp9w+buS8M9rFHUty18tF4h5P8J6bs4ev9Z80cEvXmP7FavEe5181s6ptni5txV9x7omEbJE7p0l3Tqnn08HatW2ywnHJvWuidrFMVg4ynKR1wOmq+9r9xQdvMcb0L5aLn61//FUvLT686sXkEfWRkFrZOKBdes/uXa532iJL9d8Nf0ubFDcCL+/QHX0Fiz/oDBwK3mKM6V9+9PQzljydrX+8c761R4U9dXfU+VO16iteKIqENMh60SofLyw7F6h56/NSer8PpBuLl573n5gRPiSiYvqXmd82WFL/sP5x6efWHhVWNz/q/Clb9U05h8NX1nnWTJU3RFXHbuHWI2tGV7lL5kTqdQfv2JZde177CjEUb4zpX/rXt3rjqj80NvT6F6uzppyTsiQr742FRf7IA/nD5taG6y3MCY27tUNHlHZ5xRtj+herxXsL9V8FPOc7m6dL17izEAJquy+tp/O8G3UghcWXC7+CnosP35PSJh0/q+crvhfiO3BA8R04oPgOHFB8IwYoFg+K78ABxVUPiqseFFc9KI47UCweFFc9KK56UFz1oLjqQXHcgWLxoLjqQXHVg+KqB8VVD4rjDhSLB8VVD4qrHhRXPSiuelAcd6BYPCiuelBc9aC46kFx1YPiuAPF4kFx1YPiqgfFVQ+Kqx4Uxx0oFg+Kqx4UVz0ornpQXPWgOO5AsXhQXPWguOpBcdWD4qoHxXEHisWD4qoHxVUPiqseFFc9KI47UCweFFc9KK56UFz1oLjqQXHcgWLxoLjqQXHVg+KqB8VVD4rjDhSLB8VVD4qrHhRXPSiuelAcd6BYPCiuelBc9aC46kFx1YPiuAOVouLNH0c3X4pWvaXi49650+myktGPNZ9P6uEn4x9O0apPuvjNUwWLD/jkd93e1Pb2es0mn8mj2+bFP56iVS/ktUm7wldefWh6pZRleZmjNmrXNjnl9nEj553RLu5YkrteLhL3eEL3XOlc/uKiMSVSHjnI4oNMir80p1xzX7vJo9NTfHjViyvOPZGQLXLnNOnOKfV8Oli7tk1WOC65d03ULpbJykHGk4ijdeKQ/5h2mcUHmBX/rP7xsZtffGTcTR5RHwmplY0D2qX37N7lWp5okaVCc0ubFDf8ZzAU3ymFN3iAxQd9OLqgO84s/eMP7+32DgUFk/Pjp6aq+IoXiiIhDbJetMrHC8vOBWre+ryU3u8D6V2LDx9j8WF94RVvWPVNOYfDIes8a6bKG6KqY7dw65E1o6vcJXMixbsNp2Px0cyK/1mD5pGbX7xx1R8aG7omVmdNOSdlSVbeGwuL/JEH8ofNrQ2XXJjjiTyMxUczKf76kmc0S70mj07zqrcQQj249o9kHu3dF/94Cn9WXy78CnouPnxPSpt0/Kyer/heiP8nDSi+AwcU34EDiu/AAcV34IDiuAPF4kFx1YPiqgfFVQ+Kqx4Uxx0oFg+Kqx4UVz0ornpQXPWgOO5AsXhQXPWguOpBcdWD4qoHxXEHisWD4qoHxVUPiqseFFc9KI47UCweFFc9KK56UFz1oLjqQXHcgWLxoLjqQXHVg+KqB8VVD4rjDhSLB8VVD4qrHhRXPSiuelAcd6BYPCiuelBc9aC46kFx1YPiuAPF4kFx1YPiqgfFVQ+Kqx4Uxx0oFg+Kqx4UVz0ornpQXPWgOO5AsXhQXPWguOpBcdWD4qoHxXEHisWD4qoHxVUPiqseFFc9KI47UCweFFc9KK56UFz1oLjq+wbzImw8KI3j7ovv4h09YnoGCuquiE6nK+qIK3LXXlL8a8f9n95arfsmeNAz2/QMFNRNEZuniuji3Ud7epBfGld9sPj7T2p+8Z/gQRafGCGvTdoVvrLSufzFRWNKtO+XB43Fb84evtbfmdixJHd91IOipHHVB4ufqX9YxeKtEVeceyJXjtaJQ7JykP9ypPjWAaer7mv3F1/mv7XLg6KkcdUvdhToMvUP2XcVBNw73koELjF5RH3kSqcU3mA5huJ9BYs/6Ay84m8EPhkfFCWNq56v+CSIiheKIlcCxUQXLz3vPzHDeGuXB0VJ/7ibVq35NYu3RsimnMORK3GLb8uuPS9ajcUbHxQl/cW/Uqx57mrwIItPjFbEobFNkStxX/Frh44o7fKKNz4oShpX/b/j/ffGd8j0DJQq/Fl931Eu/AoSPW6KP6sHxZ/Vg+IbMUCxeFB8Bw4ornpQXPWguOpBcdyBYvGguOpBcdWD4qoHxVUPiuMOFIsHxVUPiqseFFc9KK56UBx3oFg8KK56UFz1oLjqQXHVg+K4A8XiQXHVg+KqB8VVD4qrHhTHHSgWD4qrHhRXPSiuelBc9aA47kCxeFBc9aC46kFx1YPiqgfFcQeKxYPiqgfFVQ+Kqx4UVz0ojjtQLB5UUsXXOIq7yrhrghpj8hUF5Y5XFDTuDkVBeTmKghwTi5PgqEmieHmxuqtpHx1TY8F7ioKK31YUVPKSoqB3fq4o6MD86iRcNG/W4u+rV/at/jeVioLe+lBR0J7tioJOrFEUVLdUUVA8LD6ExZth8T1j8WZYfM9YvBkWbw+LD2HxZk6rOu95t6KgS42KguovKwpq+VpRUOdZRUHxWCye+gsWD4rFg2LxoFg8KBYPisWDYvGgWDwoFg8qoeKvPjrsyUbjpcgBa2KCZKfTZSMnNmjv+KHTP1cRtG98pvOfKoKkPHGrjZzYoOlCCPU/tU+o+KUr24teNl6KHLAmJmjzVGGr+OiguttOtW7I9SUf1DT4Y887YxQ8IyldDlvfTqODfFl1zc1tdpJMJfLcfBlfyWMOw6XIAWtiguSRg7aKjwmqeE7K+oHW/35XTFDTYd+1XRMVPCPZMWufneJjgq5mFGTM6+ENdDYk8tyaRJusyTRcihywJiZIfwJ2io8X1Lm8SEnQ12LQFyqCVmxw2Sk+JuizaSe/ffZhG0nmEi5+iOFS5IA1MUEymeK7BpVPWtGh5hldf/0BBUH7n/LZLz7qGckrA5T/ZdXEvtVfkMfzDZciB6yJCZI2i499Rq/MqLKRExt08g9SNtj4mo4JWiZ0FckHfXJMyu8G2viiNpfQF2VRia94lZRH3aFLwU+WxQTZLD4m6Hh+Y3Nzszf5oLphp3xb71fwjDS2XvExQf8aebZj5WI7SaYSem6uWbcvuO7vKHgp+MmymCC7xUcHvel/fdmIinlGe+4c+uA5Bc9I2i0+JuhP2VlLvrGTZIo/wAHF4kGxeFAsHtT/AR1owGCyetraAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-6"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6"/> </p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> @@ -322,16 +322,16 @@ objects.</p> <pre><code>## mkin version: 0.9.36 ## R version: 3.2.0 -## Date of fit: Fri Jun 5 14:20:31 2015 -## Date of summary: Fri Jun 5 14:20:31 2015 +## Date of fit: Fri Jun 19 16:21:21 2015 +## Date of summary: Fri Jun 19 16:21:21 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent ## d_m1 = + k_parent_m1 * parent - k_m1_sink * m1 ## -## Model predictions using solution type deSolve +## Model predictions using solution type odeintr ## -## Fitted with method Port using 153 model solutions performed in 0.621 s +## Fitted with method Port using 153 model solutions performed in 0.562 s ## ## Weighting: none ## @@ -370,7 +370,7 @@ objects.</p> ## parent_0 1.00000 0.6075 -0.06625 -0.1701 ## log_k_parent_sink 0.60752 1.0000 -0.08740 -0.6253 ## log_k_parent_m1 -0.06625 -0.0874 1.00000 0.4716 -## log_k_m1_sink -0.17006 -0.6253 0.47163 1.0000 +## log_k_m1_sink -0.17006 -0.6253 0.47164 1.0000 ## ## Residual standard error: 3.211 on 36 degrees of freedom ## |