Improve calculation of DT90 in endpoints(), check and test

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diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
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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]]))

-}

-```