From 556598ba543cf655cdc0a6995cc579327f9540ad Mon Sep 17 00:00:00 2001
From: Johannes Ranke
install.packages("mkin")
-If looking for the latest features, you can install directly from
-github, e.g. using the devtools
package.
-Using quick = TRUE
skips docs, multiple-architecture builds, demos, and
-vignettes, to make installation as fast and painless as possible.
require(devtools)
-install_github("jranke/mkin", quick = TRUE)
-
-
In the regulatory evaluation of chemical substances like plant protection @@ -108,7 +99,7 @@ reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.
plot.mmkin
plot.mmkin
.
mkinpredict
is performed either using the analytical solution for the case of
@@ -121,10 +112,6 @@ generated C code, seeccSolve
package. Thanks
to Karline Soetaert for her work on that.mkinfit
-internally using the modFit
function from the FME
package,
-but using the Port routine nlminb
per default.transform_odeparms
@@ -152,9 +139,7 @@ as in KinGUII and CAKE (see below). Simply add the argument
componenent for each of the observed variables will be optimised
in a second stage after the primary optimisation algorithm has converged.mkin
would not be possible without the underlying software stack consisting
-of R and the packages deSolve,
-minpack.lm and
-FME, to say the least.
It could not have been written without me being introduced to regulatory fate modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories @@ -187,7 +171,7 @@ as detailed in their guidance document from 2006, slightly updated in 2011 and BayerCropScience, which is based on the MatLab runtime environment.
The companion package -kinfit was +kinfit (now deprecated) was started in 2008 and first published on CRAN on 01 May 2010.
diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html index c959f114..ef7753f1 100644 --- a/inst/web/mccall81_245T.html +++ b/inst/web/mccall81_245T.html @@ -114,8 +114,8 @@## Unit: milliseconds
## expr min lq mean median uq
-## deSolve, not compiled 9538.4007 9570.3211 9605.6503 9602.2416 9639.2752
-## Eigenvalue based 881.9438 885.9337 901.1558 889.9236 910.7618
-## deSolve, compiled 692.0913 695.6109 697.9629 699.1304 700.8987
+## deSolve, not compiled 9508.4631 9522.5843 9634.9196 9536.7055 9698.1479
+## Eigenvalue based 872.6560 877.4544 888.3598 882.2527 896.2117
+## deSolve, compiled 698.8148 700.5031 708.8625 702.1914 713.8864
## max neval cld
-## 9676.3087 3 c
-## 931.5999 3 b
-## 702.6669 3 a
+## 9859.5902 3 b
+## 910.1707 3 a
+## 725.5815 3 a
autoplot(mb.1)
-
-We see that using the compiled model is by a factor of 13.7 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 13.6 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 13.734549
-## Eigenvalue based 1.272901
+## deSolve, not compiled 13.581348
+## Eigenvalue based 1.256428
## deSolve, compiled 1.000000
## Unit: seconds
-## expr min lq mean median uq
-## deSolve, not compiled 20.475764 20.494740 20.507391 20.513716 20.523205
-## deSolve, compiled 1.244022 1.244327 1.261983 1.244631 1.270963
+## expr min lq mean median uq
+## deSolve, not compiled 21.324080 21.368031 21.460777 21.411981 21.52913
+## deSolve, compiled 1.376772 1.414208 1.461651 1.451643 1.50409
## max neval cld
-## 20.532695 3 b
-## 1.297295 3 a
+## 21.646269 3 b
+## 1.556538 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 16.5 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 0.9.41.9000 on
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
diff --git a/inst/web/vignettes/mkin.pdf b/inst/web/vignettes/mkin.pdf
index e9ee9ed1..00940a35 100644
Binary files a/inst/web/vignettes/mkin.pdf and b/inst/web/vignettes/mkin.pdf differ
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index d9fc6e18..076ab4c5 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -10,7 +10,7 @@
-
+
Example evaluation of FOCUS Example Dataset D
@@ -64,7 +64,7 @@ img {
Example evaluation of FOCUS Example Dataset D
Johannes Ranke
-2015-11-13
+2015-12-09
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 9584aee5..9797e2f1 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -10,7 +10,7 @@
-
+
Example evaluation of FOCUS Laboratory Data L1 to L3
@@ -65,7 +65,7 @@ img {
Example evaluation of FOCUS Laboratory Data L1 to L3
Johannes Ranke
-2015-11-13
+2015-12-09
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
index 1d08173a..ba30cb0c 100644
Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index c7f4fbea..bd39ae2d 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -10,7 +10,7 @@
-
+
Performance benefit by using compiled model definitions in mkin
@@ -65,7 +65,7 @@ img {
Performance benefit by using compiled model definitions in mkin
Johannes Ranke
-2015-11-13
+2015-12-09
@@ -104,21 +104,21 @@ smb.1 <- summary(mb.1)
print(mb.1)
## Unit: milliseconds
## expr min lq mean median uq
-## deSolve, not compiled 9538.4007 9570.3211 9605.6503 9602.2416 9639.2752
-## Eigenvalue based 881.9438 885.9337 901.1558 889.9236 910.7618
-## deSolve, compiled 692.0913 695.6109 697.9629 699.1304 700.8987
+## deSolve, not compiled 9508.4631 9522.5843 9634.9196 9536.7055 9698.1479
+## Eigenvalue based 872.6560 877.4544 888.3598 882.2527 896.2117
+## deSolve, compiled 698.8148 700.5031 708.8625 702.1914 713.8864
## max neval cld
-## 9676.3087 3 c
-## 931.5999 3 b
-## 702.6669 3 a
+## 9859.5902 3 b
+## 910.1707 3 a
+## 725.5815 3 a
autoplot(mb.1)
-
-We see that using the compiled model is by a factor of 13.7 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 13.6 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 13.734549
-## Eigenvalue based 1.272901
+## deSolve, not compiled 13.581348
+## Eigenvalue based 1.256428
## deSolve, compiled 1.000000
@@ -136,19 +136,19 @@ smb.1["median"]/smb.1["deSolve, compiled", "median"
smb.2 <- summary(mb.2)
print(mb.2)
## Unit: seconds
-## expr min lq mean median uq
-## deSolve, not compiled 20.475764 20.494740 20.507391 20.513716 20.523205
-## deSolve, compiled 1.244022 1.244327 1.261983 1.244631 1.270963
+## expr min lq mean median uq
+## deSolve, not compiled 21.324080 21.368031 21.460777 21.411981 21.52913
+## deSolve, compiled 1.376772 1.414208 1.461651 1.451643 1.50409
## max neval cld
-## 20.532695 3 b
-## 1.297295 3 a
+## 21.646269 3 b
+## 1.556538 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 16.5 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 0.9.41.9000 on
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf
index e9ee9ed1..00940a35 100644
Binary files a/vignettes/mkin.pdf and b/vignettes/mkin.pdf differ
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