Fast analytical solutions for saemix, update.mmkin

Also, use logit transformation for g and for solitary formation
fractions, addressing #10.

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)