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, ...)

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.

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.

use_compiled

If set to FALSE, no compiled version of the mkinmod model is used, even if is present.

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_sink = 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_sink = 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_sink = 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_sink = 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_sink = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical")[21,]
#> time degradinol #> 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "lsoda")[21,]
#> time degradinol #> 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, method = "ode45")[21,]
#> time degradinol #> 21 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 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_sink = 0.3), c(degradinol = 100), seq(0, 20, by = 0.1))[201,]
#> time degradinol #> 201 20 0.2478752
mkinpredict(SFO, c(k_degradinol_sink = 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"))
#> Successfully compiled differential equation model from auto-generated C code.
system.time( print(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,]))
#> time parent m1 #> 201 20 4.978707 27.46227
#> User System verstrichen #> 0.004 0.000 0.004
system.time( print(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,]))
#> time parent m1 #> 201 20 4.978707 27.46227
#> User System verstrichen #> 0.002 0.000 0.002
system.time( print(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,]))
#> time parent m1 #> 201 20 4.978707 27.46227
#> User System verstrichen #> 0.047 0.000 0.047
# Predict from a fitted model f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Model cost at call 1 : 552.5739 #> Model cost at call 3 : 552.5739 #> Model cost at call 4 : 552.5739 #> Model cost at call 6 : 279.9345 #> Model cost at call 7 : 279.9344 #> Model cost at call 8 : 279.9294 #> Model cost at call 9 : 279.9294 #> Model cost at call 12 : 200.3629 #> Model cost at call 13 : 200.3629 #> Model cost at call 18 : 197.9039 #> Model cost at call 23 : 197.9039 #> Model cost at call 25 : 196.6754 #> Model cost at call 27 : 196.6754 #> Model cost at call 32 : 196.5742 #> Model cost at call 33 : 196.5742 #> Model cost at call 34 : 196.5742 #> Model cost at call 38 : 196.5361 #> Model cost at call 40 : 196.5361 #> Model cost at call 44 : 196.5336 #> Model cost at call 45 : 196.5336 #> Model cost at call 50 : 196.5334 #> Model cost at call 51 : 196.5334 #> Model cost at call 52 : 196.5334 #> Model cost at call 56 : 196.5334 #> Model cost at call 58 : 196.5334 #> Model cost at call 59 : 196.5334 #> Model cost at call 65 : 196.5334 #> Model cost at call 73 : 196.5334 #> Model cost at call 78 : 196.5334 #> Model cost at call 80 : 196.5334 #> Optimisation by method Port successfully terminated.
head(mkinpredict(f))
#> time parent m1 #> 1 0.0 82.49216 0.000000 #> 2 0.1 80.00563 1.179955 #> 3 0.2 77.59404 2.312580 #> 4 0.3 75.25515 3.399419 #> 5 0.4 72.98675 4.441969 #> 6 0.5 70.78673 5.441679