# mkin
The R package **mkin** provides calculation routines for the analysis of
chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as
needed for modelling the formation and decline of transformation products, or
if several compartments are involved.
## Installation
You can install the latest released version from
[CRAN](http://cran.r-project.org/package=mkin) from within R:
```r
install.packages("mkin")
```
If looking for the latest features, you can install directly from
[github](http://github.com/jranke/mkin), 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.
```r
require(devtools)
install_github("jranke/mkin", quick = TRUE)
```
## Background
In the regulatory evaluation of chemical substances like plant protection
products (pesticides), biocides and other chemicals, degradation data play an
important role. For the evaluation of pesticide degradation experiments,
detailed guidance and helpful tools have been developed as detailed in
'Credits and historical remarks' below.
## Usage
The simplest usage example that I can think of, using model shorthand notation
(available since mkin 0.9-32) and a built-in dataset is
```r
library(mkin)
```
```
## Loading required package: minpack.lm
## Loading required package: rootSolve
```
```r
fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
plot(fit, show_residuals = TRUE)
```
```r
# Output not shown in this README to avoid distraction
summary(fit)
```
A still very simple usage example including the definition of the same data in R
code would be
```r
example_data = data.frame(
name = rep("parent", 9),
time = c(0, 1, 3, 7, 14, 28, 63, 91, 119),
value = c(85.1, 57.9, 29.9, 14.6, 9.7, 6.6, 4, 3.9, 0.6)
)
fit2 <- mkinfit("FOMC", example_data, quiet = TRUE)
plot(fit2, show_residuals = TRUE)
```
A fairly complex usage example using another built-in dataset:
```
## Loading required package: methods
```
```r
data <- mkin_wide_to_long(schaefer07_complex_case, time = "time")
model <- mkinmod(
parent = mkinsub("SFO", c("A1", "B1", "C1"), sink = FALSE),
A1 = mkinsub("SFO", "A2"),
B1 = mkinsub("SFO"),
C1 = mkinsub("SFO"),
A2 = mkinsub("SFO"), use_of_ff = "max")
```
```
## Compiling differential equation model from auto-generated C code...
```
```r
fit3 <- mkinfit(model, data, method.modFit = "Port")
```
```
## Model cost at call 1 : 2511.655
## Model cost at call 2 : 2511.655
## Model cost at call 11 : 1436.639
## Model cost at call 12 : 1436.638
## Model cost at call 13 : 1436.566
## Model cost at call 21 : 643.6583
## Model cost at call 22 : 643.6583
## Model cost at call 23 : 643.6582
## Model cost at call 29 : 643.6576
## Model cost at call 31 : 454.0244
## Model cost at call 32 : 454.0241
## Model cost at call 34 : 454.0229
## Model cost at call 43 : 378.1144
## Model cost at call 45 : 378.1143
## Model cost at call 53 : 357.245
## Model cost at call 55 : 357.2449
## Model cost at call 56 : 357.2447
## Model cost at call 63 : 354.3415
## Model cost at call 64 : 354.3415
## Model cost at call 65 : 354.3413
## Model cost at call 73 : 332.49
## Model cost at call 74 : 332.49
## Model cost at call 81 : 332.4899
## Model cost at call 83 : 315.2962
## Model cost at call 84 : 306.3085
## Model cost at call 86 : 306.3084
## Model cost at call 87 : 306.3084
## Model cost at call 92 : 306.3083
## Model cost at call 94 : 290.6377
## Model cost at call 96 : 290.6375
## Model cost at call 98 : 290.6375
## Model cost at call 101 : 290.6371
## Model cost at call 105 : 269.09
## Model cost at call 107 : 269.0899
## Model cost at call 115 : 259.7551
## Model cost at call 120 : 259.7549
## Model cost at call 123 : 259.7547
## Model cost at call 126 : 253.7973
## Model cost at call 128 : 253.7972
## Model cost at call 137 : 251.7358
## Model cost at call 139 : 251.7358
## Model cost at call 147 : 250.7394
## Model cost at call 149 : 250.7393
## Model cost at call 157 : 249.1148
## Model cost at call 159 : 249.1148
## Model cost at call 167 : 246.8768
## Model cost at call 169 : 246.8768
## Model cost at call 177 : 244.9758
## Model cost at call 179 : 244.9758
## Model cost at call 187 : 243.2914
## Model cost at call 189 : 243.2914
## Model cost at call 190 : 243.2914
## Model cost at call 194 : 243.2914
## Model cost at call 199 : 242.9202
## Model cost at call 201 : 242.9202
## Model cost at call 202 : 242.9202
## Model cost at call 209 : 242.7695
## Model cost at call 211 : 242.7695
## Model cost at call 216 : 242.7695
## Model cost at call 219 : 242.5771
## Model cost at call 221 : 242.5771
## Model cost at call 229 : 242.4402
## Model cost at call 231 : 242.4402
## Model cost at call 239 : 242.1878
## Model cost at call 241 : 242.1878
## Model cost at call 249 : 242.0553
## Model cost at call 251 : 242.0553
## Model cost at call 256 : 242.0553
## Model cost at call 259 : 241.8761
## Model cost at call 260 : 241.7412
## Model cost at call 261 : 241.6954
## Model cost at call 264 : 241.6954
## Model cost at call 275 : 241.5982
## Model cost at call 277 : 241.5982
## Model cost at call 285 : 241.5459
## Model cost at call 287 : 241.5459
## Model cost at call 295 : 241.4837
## Model cost at call 297 : 241.4837
## Model cost at call 305 : 241.3882
## Model cost at call 306 : 241.3161
## Model cost at call 307 : 241.2315
## Model cost at call 309 : 241.2315
## Model cost at call 314 : 241.2315
## Model cost at call 317 : 240.9738
## Model cost at call 322 : 240.9738
## Model cost at call 327 : 240.8244
## Model cost at call 329 : 240.8244
## Model cost at call 337 : 240.7005
## Model cost at call 339 : 240.7005
## Model cost at call 342 : 240.7005
## Model cost at call 347 : 240.629
## Model cost at call 350 : 240.629
## Model cost at call 357 : 240.6193
## Model cost at call 358 : 240.6193
## Model cost at call 364 : 240.6193
## Model cost at call 367 : 240.6193
## Model cost at call 369 : 240.5873
## Model cost at call 374 : 240.5873
## Model cost at call 380 : 240.578
## Model cost at call 382 : 240.578
## Model cost at call 390 : 240.5723
## Model cost at call 393 : 240.5723
## Model cost at call 403 : 240.569
## Model cost at call 404 : 240.569
## Model cost at call 413 : 240.569
## Model cost at call 415 : 240.5688
## Model cost at call 416 : 240.5688
## Model cost at call 417 : 240.5688
## Model cost at call 431 : 240.5686
## Model cost at call 432 : 240.5686
## Model cost at call 434 : 240.5686
## Model cost at call 443 : 240.5686
## Model cost at call 444 : 240.5686
## Model cost at call 447 : 240.5686
## Model cost at call 449 : 240.5686
## Model cost at call 450 : 240.5686
## Model cost at call 466 : 240.5686
## Model cost at call 470 : 240.5686
## Model cost at call 485 : 240.5686
## Model cost at call 509 : 240.5686
## Optimisation by method Port successfully terminated.
```
```r
plot(fit3, show_residuals = TRUE)
```
```r
#summary(fit3) # Commented out to avoid distraction from README content
mkinparplot(fit3)
```
For more examples and to see results, have a look at the examples provided in the
[`mkinfit`](http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html)
documentation or the package vignettes referenced from the
[mkin package documentation page](http://kinfit.r-forge.r-project.org/mkin_static/index.html)
## Features
* Highly flexible model specification using
[`mkinmod`](http://kinfit.r-forge.r-project.org/mkin_static/mkinmod.html),
including equilibrium reactions and using the single first-order
reversible binding (SFORB) model, which will automatically create
two latent state variables for the observed variable.
* Model solution (forward modelling) in the function
[`mkinpredict`](http://kinfit.r-forge.r-project.org/mkin_static/mkinpredict.html)
is performed either using the analytical solution for the case of
parent only degradation, an eigenvalue based solution if only simple
first-order (SFO) or SFORB kinetics are used in the model, or
using a numeric solver from the `deSolve` package (default is `lsoda`).
These have decreasing efficiency, and are automatically chosen
by default.
* As of mkin 0.9-36, model solution for models with more than one observed
variable is based on the
[`ccSolve`](https://github.com/karlines/ccSolve) package, if installed.
This is even faster than eigenvalue based solution, at least in the example
shown in the [vignette `compiled_models`](http://rawgit.com/jranke/mkin/master/vignettes/compiled_models.html)
* Model optimisation with
[`mkinfit`](http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html)
internally using the `modFit` function from the `FME` package,
but using the Port routine `nlminb` per default.
* By default, kinetic rate constants and kinetic formation fractions are
transformed internally using
[`transform_odeparms`](http://kinfit.r-forge.r-project.org/mkin_static/transform_odeparms.html)
so their estimators can more reasonably be expected to follow
a normal distribution. This has the side effect that no constraints
are needed in the optimisation. Thanks to René Lehmann for the nice
cooperation on this, especially the isometric logration transformation
that is now used for the formation fractions.
* A side effect of this is that when parameter estimates are backtransformed
to match the model definition, confidence intervals calculated from
standard errors are also backtransformed to the correct scale, and will
not include meaningless values like negative rate constants or
formation fractions adding up to more than 1, which can not occur in
a single experiment with a single defined radiolabel position.
* Summary and plotting functions. The `summary` of an `mkinfit` object is in
fact a full report that should give enough information to be able to
approximately reproduce the fit with other tools.
* The chi-squared error level as defined in the FOCUS kinetics guidance
(see below) is calculated for each observed variable.
* I recently added iteratively reweighted least squares in a similar way
it is done in KinGUII and CAKE (see below). Simply add the argument
`reweight = "obs"` to your call to `mkinfit` and a separate variance
componenent for each of the observed variables will be optimised
in a second stage after the primary optimisation algorithm has converged.
* When a metabolite decline phase is not described well by SFO kinetics,
either IORE kinetics or SFORB kinetics can be used for the metabolite,
adding one respectively two parameters to the system.
## GUI
There is a graphical user interface that I consider useful for real work. Please
refer to its [documentation page](http://kinfit.r-forge.r-project.org/gmkin_static)
for installation instructions and a manual.
## News
Yes, there is a .
## Credits and historical remarks
`mkin` would not be possible without the underlying software stack consisting
of R and the packages [deSolve](http://cran.r-project.org/package=deSolve),
[minpack.lm](http://cran.r-project.org/package=minpack.lm) and
[FME](http://cran.r-project.org/package=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
Ltd (formerly RCC Ltd). `mkin` greatly profits from and largely follows
the work done by the
[FOCUS Degradation Kinetics Workgroup](http://focus.jrc.ec.europa.eu/dk),
as detailed in their guidance document from 2006, slightly updated in 2011.
Also, it was inspired by the first version of KinGUI developed by
BayerCropScience, which is based on the MatLab runtime environment.
The companion package
[kinfit](http://kinfit.r-forge.r-project.org/kinfit_static/index.html) was
[started in 2008](https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=2) and
[first published](http://cran.r-project.org/src/contrib/Archive/kinfit/) on
CRAN on 01 May 2010.
The first `mkin` code was
[published on 11 May 2010](https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8) and the
[first CRAN version](http://cran.r-project.org/src/contrib/Archive/mkin)
on 18 May 2010.
In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named
KinGUII whose R code is based on `mkin`, but which added, amongst other
refinements, a closed source graphical user interface (GUI), iteratively
reweighted least squares (IRLS) optimisation of the variance for each of the
observed variables, and Markov Chain Monte Carlo (MCMC) simulation
functionality, similar to what is available e.g. in the `FME` package.
Somewhat in parallel, Syngenta has sponsored the development of an `mkin` and
KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is
more limited in the model formulation, but puts more weight on usability.
CAKE is available for download from the [CAKE
website](http://projects.tessella.com/cake), where you can also
find a zip archive of the R scripts derived from `mkin`, published under the GPL
license.
Finally, there is
[KineticEval](http://github.com/zhenglei-gao/KineticEval), which contains
a further development of the scripts used for KinGUII, so the different tools
will hopefully be able to learn from each other in the future as well.
## Development
Contributions are welcome! Your
[mkin fork](https://help.github.com/articles/fork-a-repo) is just a mouse click
away... The master branch on github should always be in good shape, I implement
new features in separate branches now. If you prefer subversion, project
members for the
[r-forge project](http://r-forge.r-project.org/R/?group_id=615) are welcome as well.
Generally, the source code of the latest CRAN version should be available there.
