Add plots to compiled_models vignette, rebuild staticdocs

1 files changed, 22 insertions, 15 deletions

diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
index 8dc74692..8f6df55d 100644
--- a/vignettes/compiled_models.Rmd
+++ b/vignettes/compiled_models.Rmd
@@ -16,7 +16,7 @@ vignette: >
 

 ```{r, include = FALSE}

 library(knitr)

-opts_chunk$set(tidy = FALSE, cache = TRUE)

+opts_chunk$set(tidy = FALSE, cache = FALSE)

 ```

 

 ## Benchmark for a model that can also be solved with Eigenvalues

@@ -46,24 +46,30 @@ the microbenchmark package.
 

 ```{r benchmark_SFO_SFO}

 library("microbenchmark")

+library("ggplot2")

 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),

+  "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),

   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)

+

+smb.1 <- summary(mb.1)

+print(mb.1)

+autoplot(mb.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)` 

+`r round(smb.1[1, "median"]/smb.1[3, "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}

+rownames(smb.1) <- smb.1$expr

 smb.1["median"]/smb.1["deSolve, compiled", "median"]

 ```

 

@@ -77,17 +83,18 @@ FOMC_SFO <- mkinmod(
   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),

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

+                                    use_compiled = FALSE, quiet = TRUE),

+  "deSolve, compiled" = 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 <- summary(mb.2)

+print(mb.2)

 smb.2["median"]/smb.2["deSolve, compiled", "median"]

+autoplot(mb.2)

 ```

 

 Here we get a performance benefit of a factor of 

-`r round(smb.2["deSolve, not compiled", "median"]/smb.2["deSolve, compiled", "median"], 1)` 

+`r round(smb.2[1, "median"]/smb.2[2, "median"], 1)` 

 using the version of the differential equation model compiled from C code using

 the inline package!