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| author | Johannes Ranke <jranke@uni-bremen.de> | 2020-05-08 16:25:34 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2020-05-08 16:25:34 +0200 |
| commit | ea9b76c667620d75c5aeb4077117e722ed0bc3d6 (patch) | |
| tree | d7bdbcc4fdcf9c6a7237f311a9422df8cf6e80dd /R | |
| parent | 636dade692b8eee012004a2740616385333efc48 (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.R | 13 |
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, ...) |