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</head>

<body>
<h2>Benchmark for a model that can also be solved with Eigenvalues</h2>

<p>This evaluation is taken from the example section of mkinfit. When using an mkin version
equal to or greater than 0.9-36 and a C compiler (gcc) is available, you will see
a message that the model is being compiled from autogenerated C code when
defining a model using mkinmod. The <code>mkinmod()</code> function checks for presence of
the gcc compiler using </p>

<pre><code class="r">Sys.which(&quot;gcc&quot;)
</code></pre>

<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><code class="r">library(&quot;mkin&quot;)
</code></pre>

<pre><code>## Loading required package: minpack.lm
## Loading required package: rootSolve
## Loading required package: inline
## Loading required package: methods
## Loading required package: parallel
</code></pre>

<pre><code class="r">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>

<p>We can compare the performance of the Eigenvalue based solution against the
compiled version and the R implementation of the differential equations using
the microbenchmark package.</p>

<pre><code class="r">library(&quot;microbenchmark&quot;)
library(&quot;ggplot2&quot;)
mb.1 &lt;- microbenchmark(
  &quot;deSolve, not compiled&quot; = mkinfit(SFO_SFO, FOCUS_2006_D, 
                                    solution_type = &quot;deSolve&quot;, 
                                    use_compiled = FALSE, quiet = TRUE),
  &quot;Eigenvalue based&quot; = mkinfit(SFO_SFO, FOCUS_2006_D, 
                               solution_type = &quot;eigen&quot;, quiet = TRUE),
  &quot;deSolve, compiled&quot; = mkinfit(SFO_SFO, FOCUS_2006_D, 
                                solution_type = &quot;deSolve&quot;, quiet = TRUE),
  times = 3, control = list(warmup = 0))
</code></pre>

<pre><code>## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO,
## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
## precision.
</code></pre>

<pre><code class="r">smb.1 &lt;- summary(mb.1)
print(mb.1)
</code></pre>

<pre><code>## Unit: milliseconds
##                   expr       min        lq      mean    median        uq
##  deSolve, not compiled 4652.4576 4653.2263 4673.1220 4653.9950 4683.4543
##       Eigenvalue based  749.7771  750.1327  764.7881  750.4882  772.2936
##      deSolve, compiled  641.9152  647.6028  651.6533  653.2904  656.5223
##        max neval cld
##  4712.9136     3   c
##   794.0990     3  b 
##   659.7542     3 a
</code></pre>

<pre><code class="r">autoplot(mb.1)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk benchmark_SFO_SFO"/> </p>

<p>We see that using the compiled model is by a factor of 
7.1 
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>

<pre><code class="r">rownames(smb.1) &lt;- smb.1$expr
smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot;]
</code></pre>

<pre><code>##                         median
## deSolve, not compiled 7.123930
## Eigenvalue based      1.148782
## deSolve, compiled     1.000000
</code></pre>

<h2>Benchmark for a model that can not be solved with Eigenvalues</h2>

<p>This evaluation is also taken from the example section of mkinfit. </p>

<pre><code class="r">FOMC_SFO &lt;- mkinmod(
  parent = mkinsub(&quot;FOMC&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>

<pre><code class="r">mb.2 &lt;- microbenchmark(
  &quot;deSolve, not compiled&quot; = mkinfit(FOMC_SFO, FOCUS_2006_D, 
                                    use_compiled = FALSE, quiet = TRUE),
  &quot;deSolve, compiled&quot; = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
  times = 3, control = list(warmup = 0))
</code></pre>

<pre><code>## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO,
## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
## precision.
</code></pre>

<pre><code class="r">smb.2 &lt;- summary(mb.2)
print(mb.2)
</code></pre>

<pre><code>## Unit: seconds
##                   expr       min        lq      mean    median        uq
##  deSolve, not compiled 10.202245 10.243829 10.260112 10.285413 10.289045
##      deSolve, compiled  1.176863  1.182699  1.188555  1.188536  1.194402
##        max neval cld
##  10.292677     3   b
##   1.200267     3  a
</code></pre>

<pre><code class="r">smb.2[&quot;median&quot;]/smb.2[&quot;deSolve, compiled&quot;, &quot;median&quot;]
</code></pre>

<pre><code>##   median
## 1     NA
## 2     NA
</code></pre>

<pre><code class="r">autoplot(mb.2)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk benchmark_FOMC_SFO"/> </p>

<p>Here we get a performance benefit of a factor of 
8.7 
using the version of the differential equation model compiled from C code using
the inline package!</p>

<p>This vignette was built with mkin 0.9.39 on</p>

<pre><code>## R Under development (unstable) (2016-03-23 r70368)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 8 (jessie)
</code></pre>

<pre><code>## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz
</code></pre>

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