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"))
  # 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 verstrichen 
#>       0.004       0.000       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")[201,]))
#>     time   parent       m1
#> 201   20 4.978707 27.46227
#>        User      System verstrichen 
#>       0.002       0.000       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 verstrichen 
#>       0.039       0.000       0.040 

  
    # Predict from a fitted model
    f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Model cost at call  1 :  552.5739 
#> Model cost at call  3 :  552.5739 
#> Model cost at call  4 :  552.5739 
#> Model cost at call  6 :  279.9345 
#> Model cost at call  7 :  279.9344 
#> Model cost at call  8 :  279.9294 
#> Model cost at call  9 :  279.9294 
#> Model cost at call  12 :  200.3629 
#> Model cost at call  13 :  200.3629 
#> Model cost at call  18 :  197.9039 
#> Model cost at call  23 :  197.9039 
#> Model cost at call  25 :  196.6754 
#> Model cost at call  27 :  196.6754 
#> Model cost at call  32 :  196.5742 
#> Model cost at call  33 :  196.5742 
#> Model cost at call  34 :  196.5742 
#> Model cost at call  38 :  196.5361 
#> Model cost at call  40 :  196.5361 
#> Model cost at call  44 :  196.5336 
#> Model cost at call  45 :  196.5336 
#> Model cost at call  50 :  196.5334 
#> Model cost at call  51 :  196.5334 
#> Model cost at call  52 :  196.5334 
#> Model cost at call  56 :  196.5334 
#> Model cost at call  58 :  196.5334 
#> Model cost at call  59 :  196.5334 
#> Model cost at call  65 :  196.5334 
#> Model cost at call  73 :  196.5334 
#> Model cost at call  78 :  196.5334 
#> Model cost at call  80 :  196.5334 
#> Optimisation by method Port successfully terminated.
    head(mkinpredict(f))
#>   time   parent       m1
#> 1  0.0 82.49216 0.000000
#> 2  0.1 80.00563 1.179955
#> 3  0.2 77.59404 2.312580
#> 4  0.3 75.25515 3.399419
#> 5  0.4 72.98675 4.441969
#> 6  0.5 70.78673 5.441679

Produce predictions from a kinetic model using specific parameters

Arguments

Value

Examples

Contents

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