Produce predictions from a kinetic model using specific parameters

Source: R/mkinpredict.R

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(
  x,
  odeparms,
  odeini,
  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,
  ...
)

# 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",
  method.ode = "lsoda",
  atol = 1e-08,
  rtol = 1e-10,
  map_output = 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 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.
use_compiled

If set to FALSE, no compiled version of the mkinmod model is used, even if is present.
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.
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 = mkinsub("SFO"))
# Compare solution types
mkinpredict(SFO, c(k_degradinol = 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 = 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 = 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 = 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 = 0.3), c(degradinol = 100), 0:20,
      solution_type = "analytical")[21,]
#>    time degradinol
#> 21   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
      method = "lsoda")[21,]
#>    time degradinol
#> 21   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
      method = "ode45")[21,]
#>    time degradinol
#> 21   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 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 = 0.3), c(degradinol = 100),
      seq(0, 20, by = 0.1))[201,]
#>     time degradinol
#> 201   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 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"), use_of_ff = "min")
#> Successfully compiled differential equation model from auto-generated C code.
if(require(rbenchmark)) {
  benchmark(
    eigen = 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,],
    deSolve_compiled = 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,],
    deSolve = 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,],
    replications = 10)
}
#> Lade nötiges Paket: rbenchmark
#>               test replications elapsed relative user.self sys.self user.child
#> 3          deSolve           10   0.229   28.625     0.229        0          0
#> 2 deSolve_compiled           10   0.008    1.000     0.008        0          0
#> 1            eigen           10   0.026    3.250     0.025        0          0
#>   sys.child
#> 3         0
#> 2         0
#> 1         0

# Since mkin 0.9.49.11 we also have analytical solutions for some models, including SFO-SFO
#    deSolve = 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 = "analytical", use_compiled = FALSE)[201,],

# \dontrun{
  # Predict from a fitted model
  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
  head(mkinpredict(f))
#>   time   parent       m1
#> 1  0.0 82.49216 0.000000
#> 2  0.1 80.00563 1.179963
#> 3  0.2 77.59404 2.312596
#> 4  0.3 75.25515 3.399443
#> 5  0.4 72.98675 4.442000
#> 6  0.5 70.78673 5.441717
# }