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-rw-r--r--vignettes/compiled_models.html24
1 files changed, 15 insertions, 9 deletions
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index 53c5647f..67d0f658 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -229,8 +229,13 @@ div.tocify {
<pre><code>## gcc
## &quot;/usr/bin/gcc&quot;</code></pre>
<p>First, we build a simple degradation model for a parent compound with one metabolite.</p>
-<pre class="r"><code>library(&quot;mkin&quot;)
-SFO_SFO &lt;- mkinmod(
+<pre class="r"><code>library(&quot;mkin&quot;)</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>SFO_SFO &lt;- mkinmod(
parent = mkinsub(&quot;SFO&quot;, &quot;m1&quot;),
m1 = mkinsub(&quot;SFO&quot;))</code></pre>
<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
@@ -251,15 +256,16 @@ SFO_SFO &lt;- mkinmod(
factor_SFO_SFO &lt;- NA
print(&quot;R package benchmark is not available&quot;)
}</code></pre>
+<pre><code>## Loading required package: rbenchmark</code></pre>
<pre><code>## test replications elapsed relative user.self sys.self
-## 3 deSolve, compiled 3 2.742 1.000 2.740 0.000
-## 1 deSolve, not compiled 3 24.467 8.923 24.344 0.124
-## 2 Eigenvalue based 3 3.050 1.112 3.044 0.004
+## 3 deSolve, compiled 3 2.040 1.000 2.040 0
+## 1 deSolve, not compiled 3 14.622 7.168 14.624 0
+## 2 Eigenvalue based 3 2.478 1.215 2.480 0
## user.child sys.child
## 3 0 0
## 1 0 0
## 2 0 0</code></pre>
-<p>We see that using the compiled model is by a factor of around 9 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.</p>
+<p>We see that using the compiled model is by a factor of around 7 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.</p>
</div>
<div id="model-that-can-not-be-solved-with-eigenvalues" class="section level2">
<h2>Model that can not be solved with Eigenvalues</h2>
@@ -282,12 +288,12 @@ SFO_SFO &lt;- mkinmod(
}</code></pre>
<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
<pre><code>## test replications elapsed relative user.self sys.self
-## 2 deSolve, compiled 3 4.038 1.000 4.036 0.000
-## 1 deSolve, not compiled 3 55.209 13.672 54.888 0.324
+## 2 deSolve, compiled 3 3.500 1.000 3.500 0
+## 1 deSolve, not compiled 3 29.932 8.552 29.932 0
## user.child sys.child
## 2 0 0
## 1 0 0</code></pre>
-<p>Here we get a performance benefit of a factor of 14 using the version of the differential equation model compiled from C code!</p>
+<p>Here we get a performance benefit of a factor of 9 using the version of the differential equation model compiled from C code!</p>
<p>This vignette was built with mkin 0.9.45 on</p>
<pre><code>## R version 3.4.0 (2017-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)

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