mkinpredict.RdThis 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, ...)
| mkinmod | A kinetic model as produced by |
|---|---|
| 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 |
| use_compiled | If set to |
| atol | Absolute error tolerance, passed to |
| rtol | Absolute error tolerance, passed to |
| 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. |
A matrix in the same format as the output of ode.
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 0.2478752mkinpredict(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 0.2478752mkinpredict(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 0.2478752mkinpredict(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 0.2478752mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "lsoda")[21,]#> time degradinol #> 21 20 0.2478752mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "ode45")[21,]#> time degradinol #> 21 20 0.2478752mkinpredict(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 0.2478752mkinpredict(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"))#>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 #> 0.003 0.000 0.003system.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 0.002system.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