Produce predictions from a kinetic model using specific parameters

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.

mkinpredict(x, odeparms, odeini, outtimes = seq(0, 120, by = 0.1),
    solution_type = "deSolve", use_compiled = "auto", method.ode = "lsoda",
    atol = 1e-08, rtol = 1e-10, map_output = TRUE, ...)

Arguments

x	A kinetic model as produced by mkinmod, or a kinetic fit as fitted by mkinfit. In the latter case, the fitted parameters are used for the prediction.
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.

Value

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

Examples

  SFO <- mkinmod(degradinol = mkinsub("SFO"))
#> Error in mkinmod(degradinol = mkinsub("SFO")): konnte Funktion "mkinmod" nicht finden
  # Compare solution types
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        solution_type = "analytical")
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, solution_type = "analytical"): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        solution_type = "deSolve")
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, solution_type = "deSolve"): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        solution_type = "deSolve", use_compiled = FALSE)
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, solution_type = "deSolve", use_compiled = FALSE): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        solution_type = "eigen")
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, solution_type = "eigen"): konnte Funktion "mkinpredict" nicht finden


  # Compare integration methods to analytical solution
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        solution_type = "analytical")[21,]
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, solution_type = "analytical"): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        method = "lsoda")[21,]
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, method = "lsoda"): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        method = "ode45")[21,]
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, method = "ode45"): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
        method = "rk4")[21,]
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     0:20, method = "rk4"): konnte Funktion "mkinpredict" nicht finden
 # 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,]
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     seq(0, 20, by = 0.1)): konnte Funktion "mkinpredict" nicht finden
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
        seq(0, 20, by = 0.01))[2001,]
#> Error in mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),     seq(0, 20, by = 0.01)): konnte Funktion "mkinpredict" nicht finden

  # 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"))
#> Error in mkinmod(parent = list(type = "SFO", to = "m1"), m1 = list(type = "SFO")): konnte Funktion "mkinmod" nicht finden
  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,]))
#> Error in 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"): konnte Funktion "mkinpredict" nicht finden
#> Timing stopped at: 0 0 0.001
  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,]))
#> Error in 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"): konnte Funktion "mkinpredict" nicht finden
#> Timing stopped at: 0 0 0
  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,]))
#> Error in 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): konnte Funktion "mkinpredict" nicht finden
#> Timing stopped at: 0 0 0

  
    # Predict from a fitted model
    f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Error in mkinfit(SFO_SFO, FOCUS_2006_C): konnte Funktion "mkinfit" nicht finden
    head(mkinpredict(f))
#> Error in mkinpredict(f): konnte Funktion "mkinpredict" nicht finden