From f3f415520c89f9d8526bf6fadc862ebd44be220d Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Nov 2016 18:14:32 +0100 Subject: Remove trailing whitespace, clean headers Also ignore test.R in the top level directory, as it is not meant to be public --- vignettes/compiled_models.Rmd | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) (limited to 'vignettes/compiled_models.Rmd') diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd index 9fd39d81..18e1a462 100644 --- a/vignettes/compiled_models.Rmd +++ b/vignettes/compiled_models.Rmd @@ -25,7 +25,7 @@ This evaluation is taken from the example section of mkinfit. When using an mkin 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 `mkinmod()` function checks for presence of -the gcc compiler using +the gcc compiler using ```{r check_gcc} Sys.which("gcc") @@ -48,12 +48,12 @@ the microbenchmark package. library("microbenchmark") library("ggplot2") mb.1 <- microbenchmark( - "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, - solution_type = "deSolve", + "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, + solution_type = "deSolve", use_compiled = FALSE, quiet = TRUE), - "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D, + "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE), - "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, + "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE), times = 3, control = list(warmup = 0)) @@ -62,8 +62,8 @@ print(mb.1) autoplot(mb.1) ``` -We see that using the compiled model is by a factor of -`r round(smb.1[1, "median"]/smb.1[3, "median"], 1)` +We see that using the compiled model is by a factor of +`r round(smb.1[1, "median"]/smb.1[3, "median"], 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: @@ -75,7 +75,7 @@ smb.1["median"]/smb.1["deSolve, compiled", "median"] ## Model that can not be solved with Eigenvalues -This evaluation is also taken from the example section of mkinfit. +This evaluation is also taken from the example section of mkinfit. ```{r benchmark_FOMC_SFO, fig.height = 3} FOMC_SFO <- mkinmod( @@ -83,7 +83,7 @@ FOMC_SFO <- mkinmod( m1 = mkinsub( "SFO")) mb.2 <- microbenchmark( - "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, + "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE), "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE), times = 3, control = list(warmup = 0)) @@ -93,8 +93,8 @@ smb.2["median"]/smb.2["deSolve, compiled", "median"] autoplot(mb.2) ``` -Here we get a performance benefit of a factor of -`r round(smb.2[1, "median"]/smb.2[2, "median"], 1)` +Here we get a performance benefit of a factor of +`r round(smb.2[1, "median"]/smb.2[2, "median"], 1)` using the version of the differential equation model compiled from C code! This vignette was built with mkin `r packageVersion("mkin")` on -- cgit v1.2.3