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-rw-r--r--man/mkinpredict.Rd22
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),

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