From b5b446b718b15ccaae5b197e147fc1358f0f564e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 6 Nov 2020 00:03:29 +0100 Subject: Fast analytical solutions for saemix, update.mmkin Also, use logit transformation for g and for solitary formation fractions, addressing #10. --- man/mkinfit.Rd | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) (limited to 'man/mkinfit.Rd') diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd index 8f10ea0a..768b85d3 100644 --- a/man/mkinfit.Rd +++ b/man/mkinfit.Rd @@ -119,12 +119,11 @@ 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.} \item{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 \code{\link[=ilr]{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 \link{transform_odeparms}.} \item{quiet}{Suppress printing out the current value of the negative log-likelihood after each improvement?} @@ -233,15 +232,14 @@ SFO_SFO <- mkinmod( # 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) -- cgit v1.2.1