R/mkinmod.R
, R/mkinsub.R
mkinmod.Rd
This function is usually called using a call to mkinsub()
for each observed
variable, specifying the corresponding submodel as well as outgoing pathways
(see examples).
Print mkinmod objects in a way that the user finds his way to get to its components.
This is a convenience function to set up the lists used as arguments for
mkinmod
.
mkinmod( ..., use_of_ff = "max", name = NULL, speclist = NULL, quiet = FALSE, verbose = FALSE, dll_dir = NULL, unload = FALSE, overwrite = FALSE ) # S3 method for mkinmod print(x, ...) mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)
...  For each observed variable, a list as obtained by 

use_of_ff  Specification of the use of formation fractions in the model equations and, if applicable, the coefficient matrix. If "max", formation fractions are always used (default). If "min", a minimum use of formation fractions is made, i.e. each firstorder pathway to a metabolite has its own rate constant. 
name  A name for the model. Should be a valid R object name. 
speclist  The specification of the observed variables and their submodel types and pathways can be given as a single list using this argument. Default is NULL. 
quiet  Should messages be suppressed? 
verbose  If 
dll_dir  Directory where an DLL object, if generated internally by

unload  If a DLL from the target location in 'dll_dir' is already loaded, should that be unloaded first? 
overwrite  If a file exists at the target DLL location in 'dll_dir', should this be overwritten? 
x  An 
submodel  Character vector of length one to specify the submodel type.
See 
to  Vector of the names of the state variable to which a transformation shall be included in the model. 
sink  Should a pathway to sink be included in the model in addition to the pathways to other state variables? 
full_name  An optional name to be used e.g. for plotting fits performed with the model. You can use nonASCII characters here, but then your R code will not be portable, i.e. may produce unintended plot results on other operating systems or system configurations. 
A list of class mkinmod
for use with mkinfit()
,
containing, among others,
A vector of string representations of differential equations, one for each modelling variable.
A list containing named character vectors for each observed variable, specifying the modelling variables by which it is represented.
The content of use_of_ff
is passed on in this list component.
If generated, a function containing the solution of the degradation model.
The coefficient matrix, if the system of differential equations can be represented by one.
If generated, a compiled function calculating the derivatives as returned by cfunction.
For the definition of model types and their parameters, the equations given in the FOCUS and NAFTA guidance documents are used.
For kinetic models with more than one observed variable, a symbolic solution of the system of differential equations is included in the resulting mkinmod object in some cases, speeding up the solution.
If a C compiler is found by pkgbuild::has_compiler()
and there
is more than one observed variable in the specification, C code is generated
for evaluating the differential equations, compiled using
inline::cfunction()
and added to the resulting mkinmod object.
The IORE submodel is not well tested for metabolites. When using this model for metabolites, you may want to read the note in the help page to mkinfit.
FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradationkinetics
NAFTA Technical Working Group on Pesticides (not dated) Guidance for Evaluating and Calculating Degradation Kinetics in Environmental Media
Johannes Ranke
# Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...) SFO < mkinmod(parent = mkinsub("SFO")) # One parent compound, one metabolite, both single first order SFO_SFO < mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))#>#> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max #> Specification $spec: #> $parent #> $type: SFO; $to: m1; $sink: TRUE #> $m1 #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_parent/dt =  k_parent * parent #> d_m1/dt = + f_parent_to_m1 * k_parent * parent  k_m1 * m1#> Warning: Observations with value of zero were removed from the data# Now supplying compound names used for plotting, and write to user defined location # We need to choose a path outside the session tempdir because this gets removed DLL_dir < "~/.local/share/mkin" if (!dir.exists(DLL_dir)) dir.create(DLL_dir) SFO_SFO.2 < mkinmod( parent = mkinsub("SFO", "m1", full_name = "Test compound"), m1 = mkinsub("SFO", full_name = "Metabolite M1"), name = "SFO_SFO", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE)#># Now we can save the model and restore it in a new session saveRDS(SFO_SFO.2, file = "~/SFO_SFO.rds") # Terminate the R session here if you would like to check, and then do library(mkin) SFO_SFO.3 < readRDS("~/SFO_SFO.rds") fit_sfo_sfo < mkinfit(SFO_SFO.3, FOCUS_2006_D, quiet = TRUE, solution_type = "deSolve")#> Warning: Observations with value of zero were removed from the data# Show details of creating the C function SFO_SFO < mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), verbose = TRUE)#> Program source: #> 1: #include <R.h> #> 2: #> 3: #> 4: static double parms [3]; #> 5: #define k_parent parms[0] #> 6: #define f_parent_to_m1 parms[1] #> 7: #define k_m1 parms[2] #> 8: #> 9: void initpar(void (* odeparms)(int *, double *)) { #> 10: int N = 3; #> 11: odeparms(&N, parms); #> 12: } #> 13: #> 14: #> 15: void diffs ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) { #> 16: #> 17: f[0] =  k_parent * y[0]; #> 18: f[1] = + f_parent_to_m1 * k_parent * y[0]  k_m1 * y[1]; #> 19: }#># The symbolic solution which is available in this case is not # made for human reading but for speed of computation SFO_SFO$deg_func#> function (observed, odeini, odeparms) #> { #> predicted < numeric(0) #> with(as.list(odeparms), { #> t < observed[observed$name == "parent", "time"] #> predicted << c(predicted, SFO.solution(t, odeini["parent"], #> k_parent)) #> t < observed[observed$name == "m1", "time"] #> predicted << c(predicted, (((k_m1  k_parent) * odeini["m1"]  #> f_parent_to_m1 * k_parent * odeini["parent"]) * exp(k_m1 * #> t) + f_parent_to_m1 * k_parent * odeini["parent"] * #> exp(k_parent * t))/(k_m1  k_parent)) #> }) #> return(predicted) #> } #> <environment: 0x55555a59b978># If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par < mkinmod( parent = mkinsub("DFOP", c("M1", "M2")), M1 = mkinsub("SFO"), M2 = mkinsub("SFO"), quiet = TRUE) fit_DFOP_par_c < mkinfit(m_synth_DFOP_par, synthetic_data_for_UBA_2014[[12]]$data, quiet = TRUE) # }