diff options
Diffstat (limited to 'R/mkinfit.R')
| -rw-r--r-- | R/mkinfit.R | 18 |
1 files changed, 8 insertions, 10 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R index 1b1bb73d..7fa1c56e 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -89,12 +89,11 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' models and the break point tb of the HS model. If FALSE, zero is used as #' a lower bound for the rates in the optimisation. #' @param transform_fractions Boolean specifying if formation fractions -#' constants should be transformed in the model specification used in the -#' fitting for better compliance with the assumption of normal distribution -#' of the estimator. The default (TRUE) is to do transformations. If TRUE, -#' the g parameter of the DFOP and HS models are also transformed, as they -#' can also be seen as compositional data. The transformation used for these -#' transformations is the [ilr()] transformation. +#' should be transformed in the model specification used in the fitting for +#' better compliance with the assumption of normal distribution of the +#' estimator. The default (TRUE) is to do transformations. If TRUE, +#' the g parameter of the DFOP model is also transformed. Transformations +#' are described in [transform_odeparms]. #' @param quiet Suppress printing out the current value of the negative #' log-likelihood after each improvement? #' @param atol Absolute error tolerance, passed to [deSolve::ode()]. Default @@ -187,15 +186,14 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' #' # Fit the model quietly to the FOCUS example dataset D using defaults #' fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) -#' # Since mkin 0.9.50.3, we get a warning about non-normality of residuals, -#' # so we try an alternative error model +#' plot_sep(fit) +#' # As lower parent values appear to have lower variance, we try an alternative error model #' fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc") #' # This avoids the warning, and the likelihood ratio test confirms it is preferable #' lrtest(fit.tc, fit) #' # We can also allow for different variances of parent and metabolite as error model #' fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "obs") -#' # This also avoids the warning about non-normality, but the two-component error model -#' # has significantly higher likelihood +#' # The two-component error model has significantly higher likelihood #' lrtest(fit.obs, fit.tc) #' parms(fit.tc) #' endpoints(fit.tc) |