From 6f596ecec2c54d7d91cf3ca16b8643b64b903e57 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Fri, 13 Nov 2015 11:01:49 +0100
Subject: Add plots to compiled_models vignette, rebuild staticdocs

---
 vignettes/compiled_models.Rmd | 37 ++++++++++++++++++++++---------------
 1 file changed, 22 insertions(+), 15 deletions(-)

(limited to 'vignettes/compiled_models.Rmd')

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!
 
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