R/mkinpredict.R
mkinpredict.RdThis 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, ...)
# 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,
maxsteps = 20000,
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,
...
)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.
A numeric vector specifying the parameters used in the kinetic model, which is generally defined as a set of ordinary differential equations.
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.
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.
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.
If set to FALSE, no compiled version of the
mkinmod model is used, even if is present.
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.
Absolute error tolerance, passed to ode. Default is 1e-8, lower than in lsoda.
Absolute error tolerance, passed to ode. Default is 1e-10, much lower than in lsoda.
Maximum number of steps, passed to ode.
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.
Should it be an error if ode returns NaN values
A matrix with the numeric solution in wide format
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")[21,]
#> time degradinol
#> 20.0000000 0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
method = "ode45")[21,]
#> time degradinol
#> 20.0000000 0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
method = "rk4")[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
#> 4 analytical 5.25 0.021
#> 1 eigen 6.00 0.024
#> 3 deSolve 52.50 0.210
# \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))
#> DLSODA- At current T (=R1), MXSTEP (=I1) steps
#> taken on this call before reaching TOUT
#> In above message, I1 = 1
#>
#> In above message, R1 = 9.99904e-07
#>
#> Warning: an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps
#> Warning: Returning early. Results are accurate, as far as they go
#> Error in out[available, var]: (subscript) logical subscript too long
# }