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
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
# }