mkinpredict. mkin

+ +

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(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. +

+ +

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 
+#>   0.000   0.028   0.004 
+#> 
  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   0.002 
+#> 
  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 
+#>   0.036   0.000   0.035 
+#>

Author

+ + Johannes Ranke + +