mkin 0.9.43

Produce predictions from a kinetic model using specific parameters

Usage

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.

Description

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.

Value

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

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 
  0.024   0.048   0.010 



  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 
  0.008   0.020   0.005 



  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 
  0.132   0.004   0.138

Author

Johannes Ranke