From 218a9c55bd80fb708b15fa7196422f759bfe4b27 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Wed, 13 May 2020 16:20:23 +0200
Subject: Further formatting improvement of benchmark vignette

Also, use .rmd extension instead of .Rmd for vignettes.
---
 vignettes/web_only/compiled_models.Rmd | 141 ---------------------------------
 1 file changed, 141 deletions(-)
 delete mode 100644 vignettes/web_only/compiled_models.Rmd

(limited to 'vignettes/web_only/compiled_models.Rmd')

diff --git a/vignettes/web_only/compiled_models.Rmd b/vignettes/web_only/compiled_models.Rmd
deleted file mode 100644
index f99ea808..00000000
--- a/vignettes/web_only/compiled_models.Rmd
+++ /dev/null
@@ -1,141 +0,0 @@
----
-title: "Performance benefit by using compiled model definitions in mkin"
-author: "Johannes Ranke"
-output:
-  html_document:
-    toc: true
-    toc_float: true
-    code_folding: show
-    fig_retina: null
-date: "`r Sys.Date()`"
-vignette: >
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = FALSE)
-```
-
-## How to benefit from compiled models
-
-When using an mkin version equal to or greater than 0.9-36 and a C compiler is
-available, you will see a message that the model is being compiled from
-autogenerated C code when defining a model using mkinmod. Starting from
-version 0.9.49.9, the `mkinmod()` function checks for presence of a compiler
-using
-
-```{r check_gcc, eval = FALSE}
-pkgbuild::has_compiler()
-```
-
-In previous versions, it used `Sys.which("gcc")` for this check.
-
-On Linux, you need to have the essential build tools like make and gcc or clang
-installed. On Debian based linux distributions, these will be pulled in by installing
-the build-essential package.
-
-On MacOS, which I do not use personally, I have had reports that a compiler is
-available by default.
-
-On Windows, you need to install Rtools and have the path to its bin directory
-in your PATH variable. You do not need to modify the PATH variable when
-installing Rtools. Instead, I would recommend to put the line
-
-```{r Rprofile, eval = FALSE}
-Sys.setenv(PATH = paste("C:/Rtools/bin", Sys.getenv("PATH"), sep=";"))
-```
-into your .Rprofile startup file. This is just a text file with some R
-code that is executed when your R session starts. It has to be named .Rprofile
-and has to be located in your home directory, which will generally be your
-Documents folder. You can check the location of the home directory used by R by
-issuing
-
-```{r HOME, eval = FALSE}
-Sys.getenv("HOME")
-```
-
-## Comparison with other solution methods
-
-First, we build a simple degradation model for a parent compound with one metabolite,
-and we remove zero values from the dataset.
-
-```{r create_SFO_SFO}
-library("mkin", quietly = TRUE)
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
-FOCUS_D <- subset(FOCUS_2006_D, value != 0)
-```
-
-We can compare the performance of the Eigenvalue based solution against the
-compiled version and the R implementation of the differential equations using
-the benchmark package. In the output of below code, the warnings about zero
-being removed from the FOCUS D dataset are suppressed. Since mkin version
-0.9.49.11, an analytical solution is also implemented, which is included
-in the tests below.
-
-```{r benchmark_SFO_SFO, fig.height = 3, message = FALSE, warning = FALSE}
-if (require(rbenchmark)) {
-  b.1 <- benchmark(
-    "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "deSolve",
-       use_compiled = FALSE, quiet = TRUE),
-    "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "eigen", quiet = TRUE),
-    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "deSolve", quiet = TRUE),
-    "analytical" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "analytical",
-       use_compiled = FALSE, quiet = TRUE),
-    replications = 1, order = "relative",
-    columns = c("test", "replications", "relative", "elapsed"))
-  print(b.1)
-} else {
-  print("R package rbenchmark is not available")
-}
-```
-
-We see that using the compiled model is by more than a factor of 10 faster
-than using deSolve without compiled code.
-
-## Model without analytical solution
-
-This evaluation is also taken from the example section of mkinfit. No analytical
-solution is available for this system, and now Eigenvalue based solution
-is possible, so only deSolve using with or without compiled code is
-available.
-
-```{r benchmark_FOMC_SFO, fig.height = 3, warning = FALSE}
-if (require(rbenchmark)) {
-  FOMC_SFO <- mkinmod(
-    parent = mkinsub("FOMC", "m1"),
-    m1 = mkinsub( "SFO"))
-
-  b.2 <- benchmark(
-    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_D,
-                                      use_compiled = FALSE, quiet = TRUE),
-    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE),
-    replications = 1, order = "relative",
-    columns = c("test", "replications", "relative", "elapsed"))
-  print(b.2)
-  factor_FOMC_SFO <- round(b.2["1", "relative"])
-} else {
-  factor_FOMC_SFO <- NA
-  print("R package benchmark is not available")
-}
-```
-
-Here we get a performance benefit of a factor of
-`r factor_FOMC_SFO`
-using the version of the differential equation model compiled from C code!
-
-This vignette was built with mkin `r utils::packageVersion("mkin")` on
-
-```{r sessionInfo, echo = FALSE}
-cat(utils::capture.output(utils::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]]))
-}
-```
-- 
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