From 48a314feb6538774504c2b118cdbaededd2eb25b Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 28 Jun 2016 08:06:03 +0200 Subject: Attempts to reduce vignette size Reducing the size of some figures and switching of retina figures in the preamble only gives a small decrease in vignette size, but may be enough to get the size of the doc directory below 5 MB to avoid the NOTE in the corresponding check (which I did not get locally, nor on winbuilder using r-devel. --- vignettes/compiled_models.html | 49 +++++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 22 deletions(-) (limited to 'vignettes/compiled_models.html') diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index 5e426a7f..c3ffb035 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -20,7 +20,7 @@ - + @@ -226,8 +226,13 @@ div.tocify { 
##            gcc 
 ## "/usr/bin/gcc"

First, we build a simple degradation model for a parent compound with one metabolite.

-
library("mkin")
-SFO_SFO <- mkinmod(
+
library("mkin")

+
## Loading required package: minpack.lm

+
## Loading required package: rootSolve

+
## Loading required package: inline

+
## Loading required package: methods

+
## Loading required package: parallel

+
SFO_SFO <- mkinmod(
   parent = mkinsub("SFO", "m1"),
   m1 = mkinsub("SFO"))

 
## Successfully compiled differential equation model from auto-generated C code.

@@ -250,22 +255,22 @@ mb.1 <- microbenchmark(
 print(mb.1)
## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 25.042204 25.078629 25.467550 25.115054 25.680223
-##       Eigenvalue based  2.273059  2.277424  2.285719  2.281790  2.292049
-##      deSolve, compiled  1.878785  1.883750  1.891594  1.888716  1.897998
+##  deSolve, not compiled 13.694897 13.774112 13.820936 13.853327 13.883956
+##       Eigenvalue based  2.087861  2.089503  2.116323  2.091145  2.130555
+##      deSolve, compiled  1.794975  1.799892  1.814653  1.804808  1.824492
 ##        max neval cld
-##  26.245391     3   b
-##   2.302308     3  a 
-##   1.907281     3  a
+## 13.914585 3 c +## 2.169964 3 b +## 1.844177 3 a 
autoplot(mb.1)
-

-

We see that using the compiled model is by a factor of 13.3 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:

+

+

We see that using the compiled model is by a factor of 7.7 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:

rownames(smb.1) <- smb.1$expr
 smb.1["median"]/smb.1["deSolve, compiled", "median"]
-
##                          median
-## deSolve, not compiled 13.297425
-## Eigenvalue based       1.208117
-## deSolve, compiled      1.000000
+
##                         median
+## deSolve, not compiled 7.675788
+## Eigenvalue based      1.158652
+## deSolve, compiled     1.000000

Benchmark for a model that can not be solved with Eigenvalues

@@ -285,19 +290,19 @@ smb.1["median"]/smb.1["deSolve, compiled", "median" 
smb.2 <- summary(mb.2)
 print(mb.2)
## Unit: seconds
-##                   expr      min        lq      mean    median        uq
-##  deSolve, not compiled 53.69252 53.938844 54.137601 54.185167 54.360141
-##      deSolve, compiled  3.42508  3.526298  3.588392  3.627516  3.670048
+##                   expr       min        lq      mean   median        uq
+##  deSolve, not compiled 29.120048 29.170013 29.246607 29.21998 29.309886
+##      deSolve, compiled  3.338458  3.343954  3.379437  3.34945  3.399926
 ##        max neval cld
-##  54.535116     3   b
-##   3.712579     3  a
+## 29.399796 3 b +## 3.450402 3 a 
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##   median
 ## 1     NA
 ## 2     NA
autoplot(mb.2)
-

-

Here we get a performance benefit of a factor of 14.9 using the version of the differential equation model compiled from C code!

+

+

Here we get a performance benefit of a factor of 8.7 using the version of the differential equation model compiled from C code!

This vignette was built with mkin 0.9.43 on

## R version 3.3.1 (2016-06-21)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
-- 
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