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 
#>       0.021       0.000       0.021 

  # \dontrun{
    # Predict from a fitted model
    f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 552.5739
#> Sum of squared residuals at call 3: 552.5739
#> Sum of squared residuals at call 4: 552.5739
#> Sum of squared residuals at call 6: 279.9345
#> Sum of squared residuals at call 7: 279.9344
#> Sum of squared residuals at call 8: 279.9294
#> Sum of squared residuals at call 9: 279.9294
#> Sum of squared residuals at call 12: 200.3629
#> Sum of squared residuals at call 13: 200.3629
#> Sum of squared residuals at call 18: 197.9039
#> Sum of squared residuals at call 23: 197.9039
#> Sum of squared residuals at call 25: 196.6754
#> Sum of squared residuals at call 27: 196.6754
#> Sum of squared residuals at call 32: 196.5742
#> Sum of squared residuals at call 33: 196.5742
#> Sum of squared residuals at call 34: 196.5742
#> Sum of squared residuals at call 38: 196.5361
#> Sum of squared residuals at call 40: 196.5361
#> Sum of squared residuals at call 44: 196.5336
#> Sum of squared residuals at call 45: 196.5336
#> Sum of squared residuals at call 50: 196.5334
#> Sum of squared residuals at call 51: 196.5334
#> Sum of squared residuals at call 52: 196.5334
#> Sum of squared residuals at call 56: 196.5334
#> Sum of squared residuals at call 58: 196.5334
#> Sum of squared residuals at call 59: 196.5334
#> Sum of squared residuals at call 65: 196.5334
#> Sum of squared residuals at call 73: 196.5334
#> Negative log-likelihood at call 75: 26.64668
#> Optimisation 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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