diff options
Diffstat (limited to 'R/nlme.mmkin.R')
| -rw-r--r-- | R/nlme.mmkin.R | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R index ff1f2fff..7049a9a1 100644 --- a/R/nlme.mmkin.R +++ b/R/nlme.mmkin.R @@ -24,7 +24,7 @@ get_deg_func <- function() { #' This functions sets up a nonlinear mixed effects model for an mmkin row #' object. An mmkin row object is essentially a list of mkinfit objects that #' have been obtained by fitting the same model to a list of datasets. -#' +#' #' Note that the convergence of the nlme algorithms depends on the quality #' of the data. In degradation kinetics, we often only have few datasets #' (e.g. data for few soils) and complicated degradation models, which may @@ -34,10 +34,9 @@ get_deg_func <- function() { #' @param data Ignored, data are taken from the mmkin model #' @param fixed Ignored, all degradation parameters fitted in the #' mmkin model are used as fixed parameters -#' @param random If not specified, correlated random effects are set up -#' for all optimised degradation model parameters using the log-Cholesky -#' parameterization [nlme::pdLogChol] that is also the default of -#' the generic [nlme] method. +#' @param random If not specified, no correlations between random effects are +#' set up for the optimised degradation model parameters. This is +#' achieved by using the [nlme::pdDiag] method. #' @param groups See the documentation of nlme #' @param start If not specified, mean values of the fitted degradation #' parameters taken from the mmkin object are used @@ -135,7 +134,7 @@ nlme.mmkin <- function(model, data = "auto", function(el) eval(parse(text = paste(el, 1, sep = "~")))), random = pdDiag(fixed), groups, - start = mean_degparms(model, random = TRUE), + start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, weights = NULL, subset, method = c("ML", "REML"), na.action = na.fail, naPattern, |