Update vignettes and docs

1 files changed, 30 insertions, 24 deletions

diff --git a/vignettes/web_only/compiled_models.Rmd b/vignettes/web_only/compiled_models.Rmd
index 3f4e0097..f99ea808 100644
--- a/vignettes/web_only/compiled_models.Rmd
+++ b/vignettes/web_only/compiled_models.Rmd
@@ -56,51 +56,56 @@ issuing
 Sys.getenv("HOME")

 ```

 

-## Comparison with Eigenvalue based solutions

+## Comparison with other solution methods

 

-First, we build a simple degradation model for a parent compound with one metabolite.

+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.

+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_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)

+    "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)

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

 } else {

-  factor_SFO_SFO <- NA

   print("R package rbenchmark 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.

-

+We see that using the compiled model is by more than a factor of 10 faster

+than using deSolve without compiled code.

 

-## Model that can not be solved with Eigenvalues

+## Model without analytical solution

 

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

+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)) {

@@ -109,10 +114,11 @@ if (require(rbenchmark)) {
     m1 = mkinsub( "SFO"))

 

   b.2 <- benchmark(

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

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

                                       use_compiled = FALSE, quiet = TRUE),

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

-    replications = 3)

+    "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 {