diff options
Diffstat (limited to 'vignettes/web_only/compiled_models.Rmd')
| -rw-r--r-- | vignettes/web_only/compiled_models.Rmd | 54 |
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 {
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