From 6733555d7a9315c55001770bacc4c61c4d4f39d5 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sun, 21 Jun 2015 01:46:51 +0200 Subject: Do the t-test for untransformed parameters --- vignettes/compiled_models.html | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) (limited to 'vignettes/compiled_models.html') diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index 2f2a6edb..e6f21b09 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -77,7 +77,7 @@ img { -->

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.

+

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 compiler (gcc) is installed, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod.

library("mkin")
 SFO_SFO <- mkinmod(
   parent = list(type = "SFO", to = "m1", sink = TRUE),
@@ -94,20 +94,20 @@ mb.1 <- microbenchmark(
 smb.1 <- summary(mb.1)[-1]
 rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
 print(smb.1)
-
##                             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
+
##                            min        lq      mean    median        uq
+## deSolve, not compiled 4969.585 5033.7311 5092.7389 5097.8773 5154.3160
+## Eigenvalue based       868.731  891.7239  909.6449  914.7169  930.1018
+## deSolve, compiled     4935.049 4935.4796 4968.2150 4935.9097 4984.7978
 ##                             max neval
-## deSolve, not compiled 6242.3986     3
-## Eigenvalue based      1061.3266     3
-## deSolve, compiled      906.6155     3

+## deSolve, not compiled 5210.7547     3
+## Eigenvalue based       945.4867     3
+## deSolve, compiled     5033.6858     3

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:

smb.1["median"]/smb.1["deSolve, compiled", "median"]
-
##                         median
-## deSolve, not compiled 7.120877
-## Eigenvalue based      1.205328
-## deSolve, compiled     1.000000
+
##                          median
+## deSolve, not compiled 1.0328141
+## Eigenvalue based      0.1853188
+## deSolve, compiled     1.0000000

Benchmark for a model that can not be solved with Eigenvalues

@@ -124,16 +124,16 @@ smb.2 <- summary(mb.2)[-1] rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled") print(smb.2) 
##                             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
-##                             max neval
-## deSolve, not compiled 13.588063     3
-## deSolve, compiled      1.586778     3
+## deSolve, not compiled 11.745276 11.754288 11.820726 11.763300 11.858451 +## deSolve, compiled 1.385829 1.386407 1.400841 1.386985 1.408347 +## max neval +## deSolve, not compiled 11.95360 3 +## deSolve, compiled 1.42971 3 
smb.2["median"]/smb.2["deSolve, compiled", "median"]
-
##                         median
-## deSolve, not compiled 8.653119
-## deSolve, compiled     1.000000
-

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!

+
##                       median
+## deSolve, not compiled 8.4812
+## deSolve, compiled     1.0000
+

Here we get a performance benefit of more than a factor of 10 using the version of the differential equation model compiled from C code using the inline package!

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