diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/mkinpredict.R | 64 |
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)) |