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-rw-r--r--R/mkinpredict.R64
1 files changed, 32 insertions, 32 deletions
diff --git a/R/mkinpredict.R b/R/mkinpredict.R
index 8949e800..16ee7903 100644
--- a/R/mkinpredict.R
+++ b/R/mkinpredict.R
@@ -1,9 +1,9 @@
#' Produce predictions from a kinetic model using specific parameters
-#'
+#'
#' This function produces a time series for all the observed variables in a
#' kinetic model as specified by \code{\link{mkinmod}}, using a specific set of
#' kinetic parameters and initial values for the state variables.
-#'
+#'
#' @aliases mkinpredict mkinpredict.mkinmod mkinpredict.mkinfit
#' @param x A kinetic model as produced by \code{\link{mkinmod}}, or a kinetic
#' fit as fitted by \code{\link{mkinfit}}. In the latter case, the fitted
@@ -40,7 +40,7 @@
#' @return A matrix in the same format as the output of \code{\link{ode}}.
#' @author Johannes Ranke
#' @examples
-#'
+#'
#' SFO <- mkinmod(degradinol = mkinsub("SFO"))
#' # Compare solution types
#' mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
@@ -51,8 +51,8 @@
#' solution_type = "deSolve", use_compiled = FALSE)
#' mkinpredict(SFO, c(k_degradinol_sink = 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,
#' solution_type = "analytical")[21,]
@@ -63,14 +63,14 @@
#' mkinpredict(SFO, c(k_degradinol_sink = 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),
#' seq(0, 20, by = 0.1))[201,]
#' mkinpredict(SFO, c(k_degradinol_sink = 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"))
@@ -86,13 +86,13 @@
#' 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,]))
-#'
+#'
#' \dontrun{
#' # Predict from a fitted model
#' f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#' head(mkinpredict(f))
#' }
-#'
+#'
#' @export
mkinpredict <- function(x, odeparms, odeini,
outtimes = seq(0, 120, by = 0.1),
@@ -137,36 +137,36 @@ mkinpredict.mkinmod <- function(x,
parent.name = names(x$diffs)[[1]]
o <- switch(parent.type,
SFO = SFO.solution(outtimes,
- evalparse(parent.name),
+ evalparse(parent.name),
ifelse(x$use_of_ff == "min",
- evalparse(paste("k", parent.name, "sink", sep="_")),
- evalparse(paste("k", parent.name, sep="_")))),
+ evalparse(paste("k", parent.name, "sink", sep="_")),
+ evalparse(paste("k", parent.name, sep="_")))),
FOMC = FOMC.solution(outtimes,
- evalparse(parent.name),
- evalparse("alpha"), evalparse("beta")),
+ evalparse(parent.name),
+ evalparse("alpha"), evalparse("beta")),
IORE = IORE.solution(outtimes,
- evalparse(parent.name),
- ifelse(x$use_of_ff == "min",
- evalparse(paste("k__iore", parent.name, "sink", sep="_")),
- evalparse(paste("k__iore", parent.name, sep="_"))),
- evalparse("N_parent")),
+ evalparse(parent.name),
+ ifelse(x$use_of_ff == "min",
+ evalparse(paste("k__iore", parent.name, "sink", sep="_")),
+ evalparse(paste("k__iore", parent.name, sep="_"))),
+ evalparse("N_parent")),
DFOP = DFOP.solution(outtimes,
- evalparse(parent.name),
- evalparse("k1"), evalparse("k2"),
- evalparse("g")),
+ evalparse(parent.name),
+ evalparse("k1"), evalparse("k2"),
+ evalparse("g")),
HS = HS.solution(outtimes,
- evalparse(parent.name),
- evalparse("k1"), evalparse("k2"),
- evalparse("tb")),
+ evalparse(parent.name),
+ evalparse("k1"), evalparse("k2"),
+ evalparse("tb")),
SFORB = SFORB.solution(outtimes,
- evalparse(parent.name),
- evalparse(paste("k", parent.name, "bound", sep="_")),
- evalparse(paste("k", sub("free", "bound", parent.name), "free", sep="_")),
- evalparse(paste("k", parent.name, "sink", sep="_"))),
+ evalparse(parent.name),
+ evalparse(paste("k", parent.name, "bound", sep="_")),
+ evalparse(paste("k", sub("free", "bound", parent.name), "free", sep="_")),
+ evalparse(paste("k", parent.name, "sink", sep="_"))),
logistic = logistic.solution(outtimes,
- evalparse(parent.name),
- evalparse("kmax"), evalparse("k0"),
- evalparse("r"))
+ evalparse(parent.name),
+ evalparse("kmax"), evalparse("k0"),
+ evalparse("r"))
)
out <- data.frame(outtimes, o)
names(out) <- c("time", sub("_free", "", parent.name))

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