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Diffstat (limited to 'man/mkinpredict.Rd')
| -rw-r--r-- | man/mkinpredict.Rd | 22 |
1 files changed, 11 insertions, 11 deletions
diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd index 366d5b83..f7e4acfc 100644 --- a/man/mkinpredict.Rd +++ b/man/mkinpredict.Rd @@ -102,36 +102,36 @@ kinetic parameters and initial values for the state variables. SFO <- mkinmod(degradinol = mkinsub("SFO")) # Compare solution types -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical") -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, solution_type = "deSolve") -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, solution_type = "deSolve", use_compiled = FALSE) -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, solution_type = "eigen") # Compare integration methods to analytical solution -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, solution_type = "analytical")[21,] -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, method = "lsoda")[21,] -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, method = "ode45")[21,] -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, method = "rk4")[21,] # 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), +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), seq(0, 20, by = 0.1))[201,] -mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), seq(0, 20, by = 0.01))[2001,] # 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")) + m1 = list(type = "SFO"), use_of_ff = "min") if(require(rbenchmark)) { benchmark( eigen = mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), |