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----
-title: "Performance benefit by using compiled model definitions in mkin"
-author: "Johannes Ranke"
-date: "`r Sys.Date()`"
-output:
-  html_document:
-    css: mkin_vignettes.css
-    toc: true
-    mathjax: null
-    theme: united
-vignette: >
-  %\VignetteIndexEntry{Performance benefit by using compiled model definitions in mkin}
-  %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
----
-
-```{r, include = FALSE}
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = TRUE)
-```
-
-## 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
-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 
-
-```{r check_gcc}
-Sys.which("gcc")
-```
-First, we build a simple degradation model for a parent compound with one metabolite.
-
-```{r create_SFO_SFO}
-library("mkin")
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
-```
-
-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.
-
-
-```{r benchmark_SFO_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),
-  times = 3, control = list(warmup = 1))
-smb.1 <- summary(mb.1)[-1]
-rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
-print(smb.1)
-```
-
-We see that using the compiled model is by a factor of 
-`r round(smb.1["deSolve, not compiled", "median"]/smb.1["deSolve, compiled", "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:
-
-```{r}
-smb.1["median"]/smb.1["deSolve, compiled", "median"]
-```
-
-## Benchmark for a model that can not be solved with Eigenvalues
-
-This evaluation is also taken from the example section of mkinfit. 
-
-```{r benchmark_FOMC_SFO}
-FOMC_SFO <- mkinmod(
-  parent = mkinsub("FOMC", "m1"),
-  m1 = mkinsub( "SFO"))
-
-mb.2 <- microbenchmark(
-  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
-  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
-  times = 3, control = list(warmup = 1))
-smb.2 <- summary(mb.2)[-1]
-rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled")
-print(smb.2)
-smb.2["median"]/smb.2["deSolve, compiled", "median"]
-```
-
-Here we get a performance benefit of a factor of 
-`r round(smb.2["deSolve, not compiled", "median"]/smb.2["deSolve, compiled", "median"], 1)` 
-using the version of the differential equation model compiled from C code using
-the inline package!
-
-This vignette was built with mkin `r packageVersion("mkin")` on
-
-```{r sessionInfo, echo = FALSE}
-cat(capture.output(sessionInfo())[1:3], sep = "\n")
-if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
-  cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
-}
-```