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Produce predictions from a kinetic model using specific parameters

Source: R/mkinpredict.R
mkinpredict.Rd

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.

Usage

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

A matrix with the numeric solution in wide format

Author

Johannes Ranke

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,])
}
#>               test relative elapsed
#> 4       analytical        1   0.001
#> 2 deSolve_compiled        2   0.002
#> 1            eigen        8   0.008
#> 3          deSolve       64   0.064

# \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
# }