Description Usage Arguments Value Note Note Author(s) See Also Examples
This function uses the Flexible Modelling Environment package
FME
to create a function calculating the model cost, i.e. the
deviation between the kinetic model and the observed data. This model cost is
then minimised using the Port algorithm nlminb
,
using the specified initial or fixed parameters and starting values.
Per default, parameters in the kinetic models are internally transformed in order
to better satisfy the assumption of a normal distribution of their estimators.
In each step of the optimsation, the kinetic model is solved using the
function mkinpredict
. The variance of the residuals for each
observed variable can optionally be iteratively reweighted until convergence
using the argument reweight.method = "obs"
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  mkinfit(mkinmod, observed,
parms.ini = "auto",
state.ini = "auto",
fixed_parms = NULL, fixed_initials = names(mkinmod$diffs)[1],
from_max_mean = FALSE,
solution_type = c("auto", "analytical", "eigen", "deSolve"),
method.ode = "lsoda",
use_compiled = "auto",
method.modFit = c("Port", "Marq", "SANN", "NelderMead", "BFGS", "CG", "LBFGSB"),
maxit.modFit = "auto",
control.modFit = list(),
transform_rates = TRUE,
transform_fractions = TRUE,
plot = FALSE, quiet = FALSE, err = NULL, weight = "none",
scaleVar = FALSE,
atol = 1e8, rtol = 1e10, n.outtimes = 100,
reweight.method = NULL,
reweight.tol = 1e8, reweight.max.iter = 10,
trace_parms = FALSE, ...)

mkinmod 
A list of class 
observed 
The observed data. It has to be in the long format as described in

parms.ini 
A named vector of initial values for the parameters, including parameters
to be optimised and potentially also fixed parameters as indicated by
It is possible to only specify a subset of the parameters that the model needs. You can use the parameter lists "bparms.ode" from a previously fitted model, which contains the differential equation parameters from this model. This works nicely if the models are nested. An example is given below. 
state.ini 
A named vector of initial values for the state variables of the model. In
case the observed variables are represented by more than one model
variable, the names will differ from the names of the observed variables
(see 
fixed_parms 
The names of parameters that should not be optimised but rather kept at the
values specified in 
fixed_initials 
The names of model variables for which the initial state at time 0 should be excluded from the optimisation. Defaults to all state variables except for the first one. 
from_max_mean 
If this is set to TRUE, and the model has only one observed variable, then data before the time of the maximum observed value (after averaging for each sampling time) are discarded, and this time is subtracted from all remaining time values, so the time of the maximum observed mean value is the new time zero. 
solution_type 
If set to "eigen", the solution of the system of differential equations is
based on the spectral decomposition of the coefficient matrix in cases that
this is possible. If set to "deSolve", a numerical ode solver from package

method.ode 
The solution method passed via 
use_compiled 
If set to 
method.modFit 
The optimisation method passed to In order to optimally deal with problems where local minima occur, the "Port" algorithm is now used per default as it is less prone to get trapped in local minima and depends less on starting values for parameters than the Levenberg Marquardt variant selected by "Marq". However, "Port" needs more iterations. The former default "Marq" is the Levenberg Marquardt algorithm
The "Pseudo" algorithm is not included because it needs finite parameter bounds which are currently not supported. The "Newton" algorithm is not included because its number of iterations
can not be controlled by 
maxit.modFit 
Maximum number of iterations in the optimisation. If not "auto", this will
be passed to the method called by 
control.modFit 
Additional arguments passed to the optimisation method used by

transform_rates 
Boolean specifying if kinetic rate constants should be transformed in the model specification used in the fitting for better compliance with the assumption of normal distribution of the estimator. If TRUE, also alpha and beta parameters of the FOMC model are logtransformed, as well as k1 and k2 rate constants for the DFOP and HS models and the break point tb of the HS model. If FALSE, zero is used as a lower bound for the rates in the optimisation. 
transform_fractions 
Boolean specifying if formation fractions constants should be transformed in the
model specification used in the fitting for better compliance with the
assumption of normal distribution of the estimator. The default (TRUE) is
to do transformations. If TRUE, the g parameter of the DFOP and HS
models are also transformed, as they can also be seen as compositional
data. The transformation used for these transformations is the

plot 
Should the observed values and the numerical solutions be plotted at each stage of the optimisation? 
quiet 
Suppress printing out the current model cost after each improvement? 
err 
either 
weight 
only if 
scaleVar 
Will be passed to 
atol 
Absolute error tolerance, passed to 
rtol 
Absolute error tolerance, passed to 
n.outtimes 
The length of the dataseries that is produced by the model prediction
function 
reweight.method 
The method used for iteratively reweighting residuals, also known
as iteratively reweighted least squares (IRLS). Default is NULL,
the other method implemented is called "obs", meaning that each
observed variable is assumed to have its own variance, this is
estimated from the fit and used for weighting the residuals
in each iteration until convergence of this estimate up to

reweight.tol 
Tolerance for convergence criterion for the variance components in IRLS fits. 
reweight.max.iter 
Maximum iterations in IRLS fits. 
trace_parms 
Should a trace of the parameter values be listed? 
... 
Further arguments that will be passed to 
A list with "mkinfit" and "modFit" in the class attribute.
A summary can be obtained by summary.mkinfit
.
The implementation of iteratively reweighted least squares is inspired by the work of the KinGUII team at Bayer Crop Science (Walter Schmitt and Zhenglei Gao). A similar implemention can also be found in CAKE 2.0, which is the other GUI derivative of mkin, sponsored by Syngenta.
When using the "IORE" submodel for metabolites, fitting with "transform_rates = TRUE" (the default) often leads to failures of the numerical ODE solver. In this situation it may help to switch off the internal rate transformation.
Johannes Ranke
Plotting methods plot.mkinfit
and
mkinparplot
.
Fitting of several models to several datasets in a single call to
mmkin
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77  # Use shorthand notation for parent only degradation
fit < mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
summary(fit)
# One parent compound, one metabolite, both single first order.
# Use mkinsub for convenience in model formulation. Pathway to sink included per default.
SFO_SFO < mkinmod(
parent = mkinsub("SFO", "m1"),
m1 = mkinsub("SFO"))
# Fit the model to the FOCUS example dataset D using defaults
print(system.time(fit < mkinfit(SFO_SFO, FOCUS_2006_D,
solution_type = "eigen", quiet = TRUE)))
coef(fit)
endpoints(fit)
## Not run:
# deSolve is slower when no C compiler (gcc) was available during model generation
print(system.time(fit.deSolve < mkinfit(SFO_SFO, FOCUS_2006_D,
solution_type = "deSolve")))
coef(fit.deSolve)
endpoints(fit.deSolve)
## End(Not run)
# Use stepwise fitting, using optimised parameters from parent only fit, FOMC
## Not run:
FOMC_SFO < mkinmod(
parent = mkinsub("FOMC", "m1"),
m1 = mkinsub("SFO"))
# Fit the model to the FOCUS example dataset D using defaults
fit.FOMC_SFO < mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE)
# Use starting parameters from parent only FOMC fit
fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE)
fit.FOMC_SFO < mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE,
parms.ini = fit.FOMC$bparms.ode)
# Use stepwise fitting, using optimised parameters from parent only fit, SFORB
SFORB_SFO < mkinmod(
parent = list(type = "SFORB", to = "m1", sink = TRUE),
m1 = list(type = "SFO"))
# Fit the model to the FOCUS example dataset D using defaults
fit.SFORB_SFO < mkinfit(SFORB_SFO, FOCUS_2006_D, quiet = TRUE)
fit.SFORB_SFO.deSolve < mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve",
quiet = TRUE)
# Use starting parameters from parent only SFORB fit (not really needed in this case)
fit.SFORB = mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE)
fit.SFORB_SFO < mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
## End(Not run)
## Not run:
# Weighted fits, including IRLS
SFO_SFO.ff < mkinmod(parent = mkinsub("SFO", "m1"),
m1 = mkinsub("SFO"), use_of_ff = "max")
f.noweight < mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
summary(f.noweight)
f.irls < mkinfit(SFO_SFO.ff, FOCUS_2006_D, reweight.method = "obs", quiet = TRUE)
summary(f.irls)
f.w.mean < mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean", quiet = TRUE)
summary(f.w.mean)
f.w.value < mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), err = "value",
quiet = TRUE)
summary(f.w.value)
## End(Not run)
## Not run:
# Manual weighting
dw < FOCUS_2006_D
errors < c(parent = 2, m1 = 1)
dw$err.man < errors[FOCUS_2006_D$name]
f.w.man < mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE)
summary(f.w.man)
f.w.man.irls < mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE,
reweight.method = "obs")
summary(f.w.man.irls)
## End(Not run)

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