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(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.

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   0.024   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 
#>   0.016   0.004   0.002 
#> 
  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 
#>   0.032   0.000   0.033 
#> 

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