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-rw-r--r--R/mkinfit.R17
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.
#'

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