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authorJohannes Ranke <jranke@uni-bremen.de>2020-05-08 16:25:34 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2020-05-08 16:25:34 +0200
commitea9b76c667620d75c5aeb4077117e722ed0bc3d6 (patch)
treed7bdbcc4fdcf9c6a7237f311a9422df8cf6e80dd /R
parent636dade692b8eee012004a2740616385333efc48 (diff)
We do not need the n.outtimes argument for mkinfit
As we set the tolerance for ode() appropriately
Diffstat (limited to 'R')
-rw-r--r--R/mkinfit.R13
1 files changed, 3 insertions, 10 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R
index 61593ce5..f0738ffc 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -101,10 +101,6 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value"))
#' is 1e-8, lower than in \code{\link{lsoda}}.
#' @param rtol Absolute error tolerance, passed to \code{\link{ode}}. Default
#' is 1e-10, much lower than in \code{\link{lsoda}}.
-#' @param n.outtimes The length of the dataseries that is produced by the model
-#' prediction function \code{\link{mkinpredict}}. This impacts the accuracy
-#' of the numerical solver if that is used (see \code{solution_type}
-#' argument.
#' @param error_model If the error model is "const", a constant standard
#' deviation is assumed.
#'
@@ -248,7 +244,7 @@ mkinfit <- function(mkinmod, observed,
transform_rates = TRUE,
transform_fractions = TRUE,
quiet = FALSE,
- atol = 1e-8, rtol = 1e-10, n.outtimes = 10,
+ atol = 1e-8, rtol = 1e-10,
error_model = c("const", "obs", "tc"),
error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep", "fourstep", "IRLS", "OLS"),
reweight.tol = 1e-8, reweight.max.iter = 10,
@@ -523,11 +519,8 @@ mkinfit <- function(mkinmod, observed,
errparms_optim <- errparms
}
- # Define outtimes for model solution.
- # Include time points at which observed data are available
- outtimes = sort(unique(c(observed$time, seq(min(observed$time),
- max(observed$time),
- length.out = n.outtimes))))
+ # Unique outtimes for model solution.
+ outtimes = sort(unique(observed$time))
# Define the objective function for optimisation, including (back)transformations
cost_function <- function(P, trans = TRUE, OLS = FALSE, fixed_degparms = FALSE, fixed_errparms = FALSE, update_data = TRUE, ...)

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