Use odeintr instead of ccSolve for compiling modelsodeintr

5 files changed, 51 insertions, 65 deletions

diff --git a/vignettes/FOCUS_D.Rmd b/vignettes/FOCUS_D.Rmd
index 902d3d24..f3dd6661 100644
--- a/vignettes/FOCUS_D.Rmd
+++ b/vignettes/FOCUS_D.Rmd
@@ -27,7 +27,7 @@ kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics
 

 The call to mkinmod returns a degradation model. The differential equations represented in 

 R code can be found in the character vector `$diffs` of the `mkinmod` object. If

-the `ccSolve` 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.

 

 

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 &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;m1&quot;), m1 = mkinsub(&quot;SFO&quot;))
 </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 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" 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
 ## 
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
index 3174a23a..e2a4baa9 100644
--- a/vignettes/FOCUS_Z.pdf
+++ b/vignettes/FOCUS_Z.pdf
diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
index bac284c5..b6d54710 100644
--- a/vignettes/compiled_models.Rmd
+++ b/vignettes/compiled_models.Rmd
@@ -15,22 +15,20 @@ output:
 ```{r, include = FALSE}

 library(knitr)

 opts_chunk$set(tidy = FALSE, cache = TRUE)

-if (!require("ccSolve")) 

-  message("Please install the ccSolve package for this vignette to produce sensible output")

-

 ```

 

 # Benchmark for a model that can also be solved with Eigenvalues

 

 This evaluation is taken from the example section of mkinfit. When using an mkin version

-greater than 0.9-36 and the ccSolve package is installed and functional, you will get a

-message that the model is being compiled when defining a model using mkinmod.

+greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows,

+install Rtools), you will get a message that the model is being compiled when

+defining a model using mkinmod.

 

 ```{r create_SFO_SFO}

 library("mkin")

 SFO_SFO <- mkinmod(

   parent = list(type = "SFO", to = "m1", sink = TRUE),

-  m1 = list(type = "SFO"))

+  m1 = list(type = "SFO"), odeintr_compile = "yes")

 ```

 

 We can compare the performance of the Eigenvalue based solution against the

@@ -39,28 +37,23 @@ the microbenchmark package.
 

 

 ```{r benchmark_SFO_SFO, echo=-(1:2)}

-# Redefining the model, in order not to confuse the knitr cache which leads to segfaults

-suppressMessages(SFO_SFO <- mkinmod(

-  parent = list(type = "SFO", to = "m1", sink = TRUE),

-  m1 = list(type = "SFO")))

 library("microbenchmark")

 mb.1 <- microbenchmark(

-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, 

-          quiet = TRUE),

-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),

   mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),

+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),

+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),

   times = 3, control = list(warmup = 1))

 smb.1 <- summary(mb.1)[-1]

-rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")

+rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "odeintr, compiled")

 print(smb.1)

 ```

 

-We see that using the compiled model is almost a factor of 8 faster than using the R version

+We see that using the compiled model is more than a factor of 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:

 

 ```{r}

-smb.1["median"]/smb.1["deSolve, compiled", "median"]

+smb.1["median"]/smb.1["odeintr, compiled", "median"]

 ```

 

 # Benchmark for a model that can not be solved with Eigenvalues

@@ -73,15 +66,15 @@ FOMC_SFO <- mkinmod(
   m1 = list(type = "SFO"))

 

 mb.2 <- microbenchmark(

-  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),

-  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),

+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),

+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),

   times = 3, control = list(warmup = 1))

 smb.2 <- summary(mb.2)[-1]

-rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled")

+rownames(smb.2) <- c("deSolve, not compiled", "odeintr, compiled")

 print(smb.2)

-smb.2["median"]/smb.2["deSolve, compiled", "median"]

+smb.2["median"]/smb.2["odeintr, compiled", "median"]

 

 ```

 

 Here we get a performance benefit of more than a factor of 8 using the version

-of the differential equation model compiled from C code using the ccSolve package!

+of the differential equation model compiled from C++ code using the odeintr package!

diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index 2f2a6edb..efdbe20d 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -77,37 +77,30 @@ img {
 -->
 <div id="benchmark-for-a-model-that-can-also-be-solved-with-eigenvalues" class="section level1">
 <h1>Benchmark for a model that can also be solved with Eigenvalues</h1>
-<p>This evaluation is taken from the example section of mkinfit. When using an mkin version greater than 0.9-36 and the ccSolve package is installed and functional, you will get a message that the model is being compiled when defining a model using mkinmod.</p>
+<p>This evaluation is taken from the example section of mkinfit. When using an mkin version greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows, install Rtools), you will get a message that the model is being compiled when defining a model using mkinmod.</p>
 <pre class="r"><code>library(&quot;mkin&quot;)
 SFO_SFO &lt;- mkinmod(
   parent = list(type = &quot;SFO&quot;, to = &quot;m1&quot;, sink = TRUE),
-  m1 = list(type = &quot;SFO&quot;))</code></pre>
-<pre><code>## Compiling differential equation model from auto-generated C code...</code></pre>
+  m1 = list(type = &quot;SFO&quot;), odeintr_compile = &quot;yes&quot;)</code></pre>
+<pre><code>## Compiling 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 class="r"><code>library(&quot;microbenchmark&quot;)
-mb.1 &lt;- microbenchmark(
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = &quot;deSolve&quot;, use_compiled = FALSE, 
-          quiet = TRUE),
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = &quot;eigen&quot;, quiet = TRUE),
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = &quot;deSolve&quot;, quiet = TRUE),
-  times = 3, control = list(warmup = 1))
-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;)
+<pre class="r"><code>smb.1 &lt;- summary(mb.1)[-1]
+rownames(smb.1) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;Eigenvalue based&quot;, &quot;odeintr, compiled&quot;)
 print(smb.1)</code></pre>
 <pre><code>##                             min        lq      mean    median        uq
-## deSolve, not compiled 6192.0125 6195.3470 6211.0309 6198.6816 6220.5401
-## Eigenvalue based       956.7604 1008.7224 1026.2572 1060.6844 1061.0055
-## deSolve, compiled      869.6880  871.9315  883.4929  874.1751  890.3953
+## deSolve, not compiled 5254.1030 5261.3501 5277.1074 5268.5973 5288.6096
+## Eigenvalue based       897.1575  921.6935  930.9546  946.2296  947.8531
+## odeintr, compiled      693.6001  709.0719  719.5530  724.5438  732.5295
 ##                             max neval
-## deSolve, not compiled 6242.3986     3
-## Eigenvalue based      1061.3266     3
-## deSolve, compiled      906.6155     3</code></pre>
-<p>We see that using the compiled model is almost a factor of 8 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>
+## deSolve, not compiled 5308.6218     3
+## Eigenvalue based       949.4766     3
+## odeintr, compiled      740.5151     3</code></pre>
+<p>We see that using the compiled model is more than a factor of 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>
+<pre class="r"><code>smb.1[&quot;median&quot;]/smb.1[&quot;odeintr, compiled&quot;, &quot;median&quot;]</code></pre>
 <pre><code>##                         median
-## deSolve, not compiled 7.120877
-## Eigenvalue based      1.205328
-## deSolve, compiled     1.000000</code></pre>
+## deSolve, not compiled 7.290796
+## Eigenvalue based      1.370242
+## odeintr, compiled     1.000000</code></pre>
 </div>
 <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level1">
 <h1>Benchmark for a model that can not be solved with Eigenvalues</h1>
@@ -115,25 +108,25 @@ print(smb.1)</code></pre>
 <pre class="r"><code>FOMC_SFO &lt;- mkinmod(
   parent = list(type = &quot;FOMC&quot;, to = &quot;m1&quot;, sink = TRUE),
   m1 = list(type = &quot;SFO&quot;))</code></pre>
-<pre><code>## Compiling differential equation model from auto-generated C code...</code></pre>
+<pre><code>## Compiling differential equation model from auto-generated C++ code...</code></pre>
 <pre class="r"><code>mb.2 &lt;- microbenchmark(
-  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
-  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = &quot;deSolve&quot;, quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = &quot;odeintr&quot;, quiet = TRUE),
   times = 3, control = list(warmup = 1))
 smb.2 &lt;- summary(mb.2)[-1]
-rownames(smb.2) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;deSolve, compiled&quot;)
+rownames(smb.2) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;odeintr, compiled&quot;)
 print(smb.2)</code></pre>
 <pre><code>##                             min        lq      mean    median        uq
-## deSolve, not compiled 13.297283 13.427702 13.481155 13.558121 13.573092
-## deSolve, compiled      1.486926  1.526887  1.546851  1.566848  1.576813
+## deSolve, not compiled 11.243675 11.324875 11.382415 11.406074 11.451785
+## odeintr, compiled      1.207114  1.209908  1.239989  1.212703  1.256426
 ##                             max neval
-## deSolve, not compiled 13.588063     3
-## deSolve, compiled      1.586778     3</code></pre>
-<pre class="r"><code>smb.2[&quot;median&quot;]/smb.2[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
+## deSolve, not compiled 11.497496     3
+## odeintr, compiled      1.300149     3</code></pre>
+<pre class="r"><code>smb.2[&quot;median&quot;]/smb.2[&quot;odeintr, compiled&quot;, &quot;median&quot;]</code></pre>
 <pre><code>##                         median
-## deSolve, not compiled 8.653119
-## deSolve, compiled     1.000000</code></pre>
-<p>Here we get a performance benefit of more than a factor of 8 using the version of the differential equation model compiled from C code using the ccSolve package!</p>
+## deSolve, not compiled 9.405494
+## odeintr, compiled     1.000000</code></pre>
+<p>Here we get a performance benefit of more than a factor of 8 using the version of the differential equation model compiled from C++ code using the odeintr package!</p>
 </div>