mkinpredict. mkin 0.9.44.9000

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 
-  0.004   0.028   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")[201,]))
-
-    time   parent       m1
-201   20 4.978707 27.46227
-
-   user  system elapsed 
-  0.016   0.004   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", use_compiled = FALSE)[201,]))
-
-    time   parent       m1
-201   20 4.978707 27.46227
-
-   user  system elapsed 
-  0.032   0.000   0.035 
-

- - - -

Author