From a04cc65a18998ff5d107a52d23c9a4aad21a03aa Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 23 Mar 2016 18:33:37 +0100 Subject: Static documentation rebuilt by staticdocs::build_site() --- inst/web/vignettes/FOCUS_Z.pdf | Bin 225028 -> 225040 bytes inst/web/vignettes/compiled_models.html | 32 ++++++++++++++++---------------- inst/web/vignettes/mkin.pdf | Bin 160263 -> 160263 bytes 3 files changed, 16 insertions(+), 16 deletions(-) (limited to 'inst/web/vignettes') diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf index e2ecd1e3..8103cba4 100644 Binary files a/inst/web/vignettes/FOCUS_Z.pdf and b/inst/web/vignettes/FOCUS_Z.pdf differ diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html index 81d26b0f..756a2753 100644 --- a/inst/web/vignettes/compiled_models.html +++ b/inst/web/vignettes/compiled_models.html @@ -104,21 +104,21 @@ smb.1 <- summary(mb.1) print(mb.1) 
## Unit: milliseconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 9494.5137 9596.5859 9663.5380 9698.6581 9748.0502
-##       Eigenvalue based  955.2669  964.7481  985.7901  974.2293 1001.0517
-##      deSolve, compiled  728.4599  736.3608  750.6479  744.2616  761.7419
+##  deSolve, not compiled 9539.3064 9543.1547 9554.1987 9547.0031 9561.6448
+##       Eigenvalue based  927.5569  928.1716  943.8293  928.7864  951.9656
+##      deSolve, compiled  734.6125  737.3273  739.0161  740.0420  741.2179
 ##        max neval cld
-##  9797.4422     3   c
-##  1027.8741     3  b 
-##   779.2221     3 a
+## 9576.2865 3 c +## 975.1447 3 b +## 742.3938 3 a 
autoplot(mb.1)
-

-

We see that using the compiled model is by a factor of 13 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 a factor of 12.9 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:

rownames(smb.1) <- smb.1$expr
 smb.1["median"]/smb.1["deSolve, compiled", "median"]
##                          median
-## deSolve, not compiled 13.031249
-## Eigenvalue based       1.308988
+## deSolve, not compiled 12.900624
+## Eigenvalue based       1.255046
 ## deSolve, compiled      1.000000
@@ -137,18 +137,18 @@ smb.2 <- summary(mb.2) print(mb.2) 
## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 20.473617 20.530414 20.676143 20.587210 20.777405
-##      deSolve, compiled  1.332632  1.334693  1.336846  1.336755  1.338953
+##  deSolve, not compiled 20.728228 20.867978 20.959811 21.007729 21.075602
+##      deSolve, compiled  1.343219  1.382365  1.399697  1.421511  1.427936
 ##        max neval cld
-##  20.967601     3   b
-##   1.341152     3  a
+## 21.143476 3 b +## 1.434362 3 a 
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##   median
 ## 1     NA
 ## 2     NA
autoplot(mb.2)
-

-

Here we get a performance benefit of a factor of 15.4 using the version of the differential equation model compiled from C code using the inline package!

+

+

Here we get a performance benefit of a factor of 14.8 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.42 on

## R version 3.2.4 Revised (2016-03-16 r70336)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
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