Produce predictions from a kinetic model using specifc parameters

Usage

mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve", use_compiled = "auto", method.ode = "lsoda", atol = 1e-08, rtol = 1e-10, map_output = TRUE, ...)

Arguments

mkinmod
A kinetic model as produced by mkinmod.
odeparms
A numeric vector specifying the parameters used in the kinetic model, which is generally defined as a set of ordinary differential equations.
odeini
A numeric vectory containing the initial values of the state variables of the model. Note that the state variables can differ from the observed variables, for example in the case of the SFORB model.
outtimes
A numeric vector specifying the time points for which model predictions should be generated.
solution_type
The method that should be used for producing the predictions. This should generally be "analytical" if there is only one observed variable, and usually "deSolve" in the case of several observed variables. The third possibility "eigen" is faster but not applicable to some models e.g. using FOMC for the parent compound.
method.ode
The solution method passed via mkinpredict to ode in case the solution type is "deSolve". The default "lsoda" is performant, but sometimes fails to converge.
use_compiled
If set to FALSE, no compiled version of the mkinmod model is used, even if is present.
atol
Absolute error tolerance, passed to ode. Default is 1e-8, lower than in lsoda.
rtol
Absolute error tolerance, passed to ode. Default is 1e-10, much lower than in lsoda.
map_output
Boolean to specify if the output should list values for the observed variables (default) or for all state variables (if set to FALSE).
...
Further arguments passed to the ode solver in case such a solver is used.

Description

This function produces a time series for all the observed variables in a kinetic model as specified by mkinmod, using a specific set of kinetic parameters and initial values for the state variables.

Value

A matrix in the same format as the output of ode.

Examples

SFO <- mkinmod(degradinol = list(type = "SFO")) # Compare solution types mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical")
time degradinol 1 0 100.0000000 2 1 74.0818221 3 2 54.8811636 4 3 40.6569660 5 4 30.1194212 6 5 22.3130160 7 6 16.5298888 8 7 12.2456428 9 8 9.0717953 10 9 6.7205513 11 10 4.9787068 12 11 3.6883167 13 12 2.7323722 14 13 2.0241911 15 14 1.4995577 16 15 1.1108997 17 16 0.8229747 18 17 0.6096747 19 18 0.4516581 20 19 0.3345965 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "deSolve")
time degradinol 1 0 100.0000000 2 1 74.0818221 3 2 54.8811636 4 3 40.6569660 5 4 30.1194212 6 5 22.3130160 7 6 16.5298888 8 7 12.2456428 9 8 9.0717953 10 9 6.7205513 11 10 4.9787068 12 11 3.6883167 13 12 2.7323722 14 13 2.0241911 15 14 1.4995577 16 15 1.1108996 17 16 0.8229747 18 17 0.6096747 19 18 0.4516581 20 19 0.3345965 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "deSolve", use_compiled = FALSE)
time degradinol 1 0 100.0000000 2 1 74.0818221 3 2 54.8811636 4 3 40.6569660 5 4 30.1194212 6 5 22.3130160 7 6 16.5298888 8 7 12.2456428 9 8 9.0717953 10 9 6.7205513 11 10 4.9787068 12 11 3.6883167 13 12 2.7323722 14 13 2.0241911 15 14 1.4995577 16 15 1.1108996 17 16 0.8229747 18 17 0.6096747 19 18 0.4516581 20 19 0.3345965 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "eigen")
time degradinol 1 0 100.0000000 2 1 74.0818221 3 2 54.8811636 4 3 40.6569660 5 4 30.1194212 6 5 22.3130160 7 6 16.5298888 8 7 12.2456428 9 8 9.0717953 10 9 6.7205513 11 10 4.9787068 12 11 3.6883167 13 12 2.7323722 14 13 2.0241911 15 14 1.4995577 16 15 1.1108997 17 16 0.8229747 18 17 0.6096747 19 18 0.4516581 20 19 0.3345965 21 20 0.2478752
# Compare integration methods to analytical solution mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical")[21,]
time degradinol 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "lsoda")[21,]
time degradinol 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "ode45")[21,]
time degradinol 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "rk4")[21,]
time degradinol 21 20 0.2480043
# rk4 is not as precise here # The number of output times used to make a lot of difference until the # default for atol was adjusted mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), seq(0, 20, by = 0.1))[201,]
time degradinol 201 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), seq(0, 20, by = 0.01))[2001,]
time degradinol 2001 20 0.2478752
# Check compiled model versions - they are faster than the eigenvalue based solutions! SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"), m1 = list(type = "SFO"))
Successfully compiled differential equation model from auto-generated C code.
system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "eigen")[201,]))
time parent m1 201 20 4.978707 27.46227
user system elapsed 0.008 0.020 0.004
system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "deSolve")[201,]))
time parent m1 201 20 4.978707 27.46227
user system elapsed 0.000 0.024 0.003
system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "deSolve", use_compiled = FALSE)[201,]))
time parent m1 201 20 4.978707 27.46227
user system elapsed 0.052 0.000 0.052

Author

Johannes Ranke