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 0.003
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 0.001
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 #> 0.021 0.000 0.021
# \dontrun{ # Predict from a fitted model f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 552.5739 #> Sum of squared residuals at call 3: 552.5739 #> Sum of squared residuals at call 4: 552.5739 #> Sum of squared residuals at call 6: 279.9345 #> Sum of squared residuals at call 7: 279.9344 #> Sum of squared residuals at call 8: 279.9294 #> Sum of squared residuals at call 9: 279.9294 #> Sum of squared residuals at call 12: 200.3629 #> Sum of squared residuals at call 13: 200.3629 #> Sum of squared residuals at call 18: 197.9039 #> Sum of squared residuals at call 23: 197.9039 #> Sum of squared residuals at call 25: 196.6754 #> Sum of squared residuals at call 27: 196.6754 #> Sum of squared residuals at call 32: 196.5742 #> Sum of squared residuals at call 33: 196.5742 #> Sum of squared residuals at call 34: 196.5742 #> Sum of squared residuals at call 38: 196.5361 #> Sum of squared residuals at call 40: 196.5361 #> Sum of squared residuals at call 44: 196.5336 #> Sum of squared residuals at call 45: 196.5336 #> Sum of squared residuals at call 50: 196.5334 #> Sum of squared residuals at call 51: 196.5334 #> Sum of squared residuals at call 52: 196.5334 #> Sum of squared residuals at call 56: 196.5334 #> Sum of squared residuals at call 58: 196.5334 #> Sum of squared residuals at call 59: 196.5334 #> Sum of squared residuals at call 65: 196.5334 #> Sum of squared residuals at call 73: 196.5334 #> Negative log-likelihood at call 75: 26.64668
#> Optimisation 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
# }