Another overhaul of analytical solutions

Still in preparation for analytical solutions of coupled models

1 files changed, 0 insertions, 59 deletions

diff --git a/R/logistic.solution.R b/R/logistic.solution.R
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-#' Logistic kinetics
-#' 
-#' Function describing exponential decline from a defined starting value, with
-#' an increasing rate constant, supposedly caused by microbial growth
-#' 
-#' @param t Time.
-#' @param parent.0 Starting value for the response variable at time zero.
-#' @param kmax Maximum rate constant.
-#' @param k0 Minumum rate constant effective at time zero.
-#' @param r Growth rate of the increase in the rate constant.
-#' @return The value of the response variable at time \code{t}.
-#' @note The solution of the logistic model reduces to the
-#'   \code{\link{SFO.solution}} if \code{k0} is equal to \code{kmax}.
-#' @references FOCUS (2014) \dQuote{Generic guidance for Estimating Persistence
-#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,
-#'   Version 1.1, 18 December 2014
-#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}
-#' @examples
-#' 
-#'   # Reproduce the plot on page 57 of FOCUS (2014)
-#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),
-#'        from = 0, to = 100, ylim = c(0, 100),
-#'        xlab = "Time", ylab = "Residue")
-#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),
-#'        from = 0, to = 100, add = TRUE, lty = 2, col = 2)
-#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),
-#'        from = 0, to = 100, add = TRUE, lty = 3, col = 3)
-#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),
-#'        from = 0, to = 100, add = TRUE, lty = 4, col = 4)
-#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),
-#'        from = 0, to = 100, add = TRUE, lty = 5, col = 5)
-#'   legend("topright", inset = 0.05,
-#'          legend = paste0("k0 = ", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),
-#'                          ", r = ", c(0.2, 0.4, 0.8, 0.2, 0.2)),
-#'          lty = 1:5, col = 1:5)
-#' 
-#'   # Fit with synthetic data
-#'   logistic <- mkinmod(parent = mkinsub("logistic"))
-#' 
-#'   sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-#'   parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2)
-#'   d_logistic <- mkinpredict(logistic,
-#'     parms_logistic, c(parent = 100),
-#'     sampling_times)
-#'   d_2_1 <- add_err(d_logistic,
-#'     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
-#'     n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]
-#' 
-#'   m <- mkinfit("logistic", d_2_1, quiet = TRUE)
-#'   plot_sep(m)
-#'   summary(m)$bpar
-#'   endpoints(m)$distimes
-#' 
-#' @export
-logistic.solution <- function(t, parent.0, kmax, k0, r)
-{
-	parent = parent.0 * (kmax / (kmax - k0 + k0 * exp (r * t))) ^(kmax/r)
-}