Produce predictions from a kinetic model using specific parameters

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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.

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mkinpredict(x, odeparms, odeini, outtimes, ...)
-
-# S3 method for mkinmod
-mkinpredict(
-  x,
-  odeparms = c(k_parent_sink = 0.1),
-  odeini = c(parent = 100),
-  outtimes = seq(0, 120, by = 0.1),
-  solution_type = "deSolve",
-  use_compiled = "auto",
-  use_symbols = FALSE,
-  method.ode = "lsoda",
-  atol = 1e-08,
-  rtol = 1e-10,
-  maxsteps = 20000L,
-  map_output = TRUE,
-  na_stop = TRUE,
-  ...
-)
-
-# S3 method for mkinfit
-mkinpredict(
-  x,
-  odeparms = x$bparms.ode,
-  odeini = x$bparms.state,
-  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 vector 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.
...: Further arguments passed to the ode solver in case such a -solver is used.
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 fast in comparison to uncompiled -ODE models, but not applicable to some models, e.g. using FOMC for the -parent compound.
use_compiled: If set to FALSE, no compiled version of the -mkinmod model is used, even if is present.
use_symbols: If set to TRUE (default), symbol info present in -the mkinmod object is used if available for accessing compiled code
method.ode: The solution method passed via mkinpredict to ode] in -case the solution type is "deSolve" and we are not using compiled code. -When using compiled code, only lsoda is supported.
atol: Absolute error tolerance, passed to the ode solver.
rtol: Absolute error tolerance, passed to the ode solver.
maxsteps: Maximum number of steps, passed to the ode solver.
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). Setting this to FALSE has no effect for analytical solutions, -as these always return mapped output.
na_stop: Should it be an error if ode returns NaN values

Value

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A matrix with the numeric solution in wide format

Author

Johannes Ranke

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Examples


-SFO <- mkinmod(degradinol = mkinsub("SFO"))
-# Compare solution types
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")
-#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108997
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "deSolve")
-#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108996
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "deSolve", use_compiled = FALSE)
-#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108996
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "eigen")
-#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108997
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
-
-# Compare integration methods to analytical solution
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")[21,]
-#>       time degradinol 
-#> 20.0000000  0.2478752 
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "lsoda", use_compiled = FALSE)[21,]
-#>       time degradinol 
-#> 20.0000000  0.2478752 
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "ode45", use_compiled = FALSE)[21,]
-#>       time degradinol 
-#> 20.0000000  0.2478752 
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "rk4", use_compiled = FALSE)[21,]
-#>       time degradinol 
-#> 20.0000000  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 = 0.3), c(degradinol = 100),
-      seq(0, 20, by = 0.1))[201,]
-#>       time degradinol 
-#> 20.0000000  0.2478752 
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
-      seq(0, 20, by = 0.01))[2001,]
-#>       time degradinol 
-#> 20.0000000  0.2478752 
-
-# Comparison of the performance of solution types
-SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
-                  m1 = list(type = "SFO"), use_of_ff = "max")
-#> Temporary DLL for differentials generated and loaded
-if(require(rbenchmark)) {
-  benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),
-    eigen = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "eigen")[201,],
-    deSolve_compiled = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "deSolve")[201,],
-    deSolve = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "deSolve", use_compiled = FALSE)[201,],
-    analytical = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "analytical", use_compiled = FALSE)[201,])
-}
-#> Loading required package: rbenchmark
-#>               test relative elapsed
-#> 2 deSolve_compiled     1.00   0.004
-#> 1            eigen     4.00   0.016
-#> 4       analytical     4.25   0.017
-#> 3          deSolve    40.75   0.163
-
-# \dontrun{
-  # Predict from a fitted model
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
-  head(mkinpredict(f))
-#> Error in !is.null(x$symbols) & use_symbols: operations are possible only for numeric, logical or complex types
-# }
-
-

- -