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       0.001 
  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", use_compiled = FALSE)[201,]))
#>     time   parent       m1
#> 201   20 4.978707 27.46227
#>        User      System verstrichen 
#>        0.04        0.00        0.04 

  
    # Predict from a fitted model
    f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Negative log-likelihood at call 1: 552.5739
#> Negative log-likelihood at call 3: 552.5739
#> Negative log-likelihood at call 4: 552.5739
#> Negative log-likelihood at call 6: 279.9345
#> Negative log-likelihood at call 7: 279.9344
#> Negative log-likelihood at call 8: 279.9294
#> Negative log-likelihood at call 9: 279.9294
#> Negative log-likelihood at call 12: 200.3629
#> Negative log-likelihood at call 13: 200.3629
#> Negative log-likelihood at call 18: 197.9039
#> Negative log-likelihood at call 25: 196.6754
#> Negative log-likelihood at call 27: 196.6754
#> Negative log-likelihood at call 32: 196.5742
#> Negative log-likelihood at call 33: 196.5742
#> Negative log-likelihood at call 34: 196.5742
#> Negative log-likelihood at call 38: 196.5361
#> Negative log-likelihood at call 40: 196.5361
#> Negative log-likelihood at call 44: 196.5336
#> Negative log-likelihood at call 45: 196.5336
#> Negative log-likelihood at call 50: 196.5334
#> Negative log-likelihood at call 51: 196.5334
#> Negative log-likelihood at call 52: 196.5334
#> Negative log-likelihood at call 56: 196.5334
#> Negative log-likelihood at call 58: 196.5334
#> Negative log-likelihood at call 59: 196.5334
#> Negative log-likelihood at call 65: 196.5334
#> Negative log-likelihood at call 75: 26.64668
#> Negative log-likelihood at call 103: 26.64668
#> Optimisation successfully terminated.
    head(mkinpredict(f))
#>   time   parent       m1
#> 1  0.0 82.49216 0.000000
#> 2  0.1 80.00563 1.179956
#> 3  0.2 77.59404 2.312583
#> 4  0.3 75.25515 3.399424
#> 5  0.4 72.98675 4.441974
#> 6  0.5 70.78673 5.441685

Produce predictions from a kinetic model using specific parameters

Arguments

Value

Examples

Contents

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