Remove two vignettes from the package but not from docs

- Rebuild static documentation
- Adapt test to new approach to two component error model
  where the model is inadequate

1 files changed, 106 insertions, 0 deletions

diff --git a/vignettes/web_only/compiled_models.Rmd b/vignettes/web_only/compiled_models.Rmd
new file mode 100644
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+++ b/vignettes/web_only/compiled_models.Rmd
@@ -0,0 +1,106 @@
+---

+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)

+```

+

+## 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", quietly = TRUE)

+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 benchmark package.

+

+

+```{r benchmark_SFO_SFO, fig.height = 3}

+if (require(rbenchmark)) {

+  b.1 <- benchmark(

+    "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,

+                                 solution_type = "eigen", quiet = TRUE),

+    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,

+                                  solution_type = "deSolve", quiet = TRUE),

+    replications = 3)

+  print(b.1)

+  factor_SFO_SFO <- round(b.1["1", "relative"])

+} else {

+  factor_SFO_SFO <- NA

+  print("R package benchmark is not available")

+}

+```

+

+We see that using the compiled model is by a factor of around

+`r factor_SFO_SFO`

+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.

+

+

+## Model that can not be solved with Eigenvalues

+

+This evaluation is also taken from the example section of mkinfit.

+

+```{r benchmark_FOMC_SFO, fig.height = 3}

+if (require(rbenchmark)) {

+  FOMC_SFO <- mkinmod(

+    parent = mkinsub("FOMC", "m1"),

+    m1 = mkinsub( "SFO"))

+

+  b.2 <- benchmark(

+    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,

+                                      use_compiled = FALSE, quiet = TRUE),

+    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),

+    replications = 3)

+  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]]))

+}

+```