diff options
Diffstat (limited to 'R/mkinfit.R')
| -rw-r--r-- | R/mkinfit.R | 17 |
1 files changed, 8 insertions, 9 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R index 0f478910..8231cd00 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -68,12 +68,11 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' coefficient matrix in cases that this is possible. If set to "deSolve", a #' numerical ode solver from package \code{\link{deSolve}} is used. If set to #' "analytical", an analytical solution of the model is used. This is only -#' implemented for simple degradation experiments with only one state -#' variable, i.e. with no metabolites. The default is "auto", which uses -#' "analytical" if possible, otherwise "deSolve" if a compiler is present, -#' and "eigen" if no compiler is present and the model can be expressed using -#' eigenvalues and eigenvectors. This argument is passed on to the helper -#' function \code{\link{mkinpredict}}. +#' implemented for relatively simple degradation models. The default is +#' "auto", which uses "analytical" if possible, otherwise "deSolve" if a +#' compiler is present, and "eigen" if no compiler is present and the model +#' can be expressed using eigenvalues and eigenvectors. This argument is +#' passed on to the helper function \code{\link{mkinpredict}}. #' @param method.ode The solution method passed via \code{\link{mkinpredict}} #' to \code{\link{ode}} in case the solution type is "deSolve". The default #' "lsoda" is performant, but sometimes fails to converge. @@ -118,9 +117,9 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' least squares fitting ("OLS") is selected. If the error model is "obs", or #' "tc", the "d_3" algorithm is selected. #' -#' The algorithm "d_3" will directly minimize the negative log-likelihood and -#' - independently - also use the three step algorithm described below. The -#' fit with the higher likelihood is returned. +#' The algorithm "d_3" will directly minimize the negative log-likelihood +#' and independently also use the three step algorithm described below. +#' The fit with the higher likelihood is returned. #' #' The algorithm "direct" will directly minimize the negative log-likelihood. #' |