aboutsummaryrefslogtreecommitdiff
path: root/inst/web/vignettes
diff options
context:
space:
mode:
authorJohannes Ranke <jranke@uni-bremen.de>2015-11-09 10:16:17 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2015-11-09 10:16:17 +0100
commit97b4b0a109d55080c781d9bbecec59b56f7037b5 (patch)
tree691ea78fe86cf59f0eeabaadf9e205a97c505dcc /inst/web/vignettes
parente8b63d52bb68dc37d3e6c933021e29b7ea7d7a23 (diff)
Update staticdocsv0.9-41
Diffstat (limited to 'inst/web/vignettes')
-rw-r--r--inst/web/vignettes/FOCUS_Z.pdfbin224802 -> 224775 bytes
-rw-r--r--inst/web/vignettes/compiled_models.html34
-rw-r--r--inst/web/vignettes/mkin.pdfbin160268 -> 160268 bytes
3 files changed, 17 insertions, 17 deletions
diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf
index 1234de20..31480019 100644
--- a/inst/web/vignettes/FOCUS_Z.pdf
+++ b/inst/web/vignettes/FOCUS_Z.pdf
Binary files differ
diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html
index 92919da9..7722d95a 100644
--- a/inst/web/vignettes/compiled_models.html
+++ b/inst/web/vignettes/compiled_models.html
@@ -99,18 +99,18 @@ smb.1 &lt;- summary(mb.1)[-1]
rownames(smb.1) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;Eigenvalue based&quot;, &quot;deSolve, compiled&quot;)
print(smb.1)</code></pre>
<pre><code>## min lq mean median uq
-## deSolve, not compiled 9307.3194 9319.9546 9332.8171 9332.5899 9345.5659
-## Eigenvalue based 855.3608 855.8081 869.4725 856.2555 876.5283
-## deSolve, compiled 686.6143 687.9256 698.0279 689.2369 703.7346
+## deSolve, not compiled 9442.5119 9447.2060 9458.3420 9451.9001 9466.2571
+## Eigenvalue based 868.6312 872.4552 895.3422 876.2792 908.6977
+## deSolve, compiled 691.9663 697.5653 701.1004 703.1643 705.6674
## max neval cld
-## deSolve, not compiled 9358.5420 3 c
-## Eigenvalue based 896.8012 3 b
-## deSolve, compiled 718.2324 3 a</code></pre>
-<p>We see that using the compiled model is by a factor of 13.5 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>
+## deSolve, not compiled 9480.6141 3 c
+## Eigenvalue based 941.1163 3 b
+## deSolve, compiled 708.1706 3 a</code></pre>
+<p>We see that using the compiled model is by a factor of 13.4 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 class="r"><code>smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
<pre><code>## median
-## deSolve, not compiled 13.540468
-## Eigenvalue based 1.242324
+## deSolve, not compiled 13.441951
+## Eigenvalue based 1.246194
## deSolve, compiled 1.000000</code></pre>
</div>
<div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2">
@@ -127,17 +127,17 @@ print(smb.1)</code></pre>
smb.2 &lt;- summary(mb.2)[-1]
rownames(smb.2) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;deSolve, compiled&quot;)
print(smb.2)</code></pre>
-<pre><code>## min lq mean median uq
-## deSolve, not compiled 20.130709 20.147223 20.180429 20.163737 20.205289
-## deSolve, compiled 1.235864 1.255748 1.267458 1.275632 1.283255
-## max neval cld
-## deSolve, not compiled 20.246841 3 b
-## deSolve, compiled 1.290878 3 a</code></pre>
+<pre><code>## min lq mean median uq
+## deSolve, not compiled 20.444632 20.48824 20.557595 20.531857 20.614077
+## deSolve, compiled 1.251733 1.25179 1.275227 1.251846 1.286973
+## max neval cld
+## deSolve, not compiled 20.6963 3 b
+## deSolve, compiled 1.3221 3 a</code></pre>
<pre class="r"><code>smb.2[&quot;median&quot;]/smb.2[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
<pre><code>## median
-## deSolve, not compiled 15.80686
+## deSolve, not compiled 16.40126
## deSolve, compiled 1.00000</code></pre>
-<p>Here we get a performance benefit of a factor of 15.8 using the version of the differential equation model compiled from C code using the inline package!</p>
+<p>Here we get a performance benefit of a factor of 16.4 using the version of the differential equation model compiled from C code using the inline package!</p>
<p>This vignette was built with mkin 0.9.41 on</p>
<pre><code>## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
diff --git a/inst/web/vignettes/mkin.pdf b/inst/web/vignettes/mkin.pdf
index 9c1a03f3..9f9f9af0 100644
--- a/inst/web/vignettes/mkin.pdf
+++ b/inst/web/vignettes/mkin.pdf
Binary files differ

Contact - Imprint