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-rw-r--r--R/mkinpredict.R22
1 files changed, 11 insertions, 11 deletions
diff --git a/R/mkinpredict.R b/R/mkinpredict.R
index 0f8e83bb..df51dbe3 100644
--- a/R/mkinpredict.R
+++ b/R/mkinpredict.R
@@ -43,36 +43,36 @@
#'
#' 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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