From 0b98c459c30a0629a728acf6b311de035c55fb64 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 18 Jul 2018 15:18:30 +0200 Subject: Correct references to the Rocke and Lorenzato model Rename 'sigma_rl' to 'sigma_twocomp' as the Rocke and Lorenzato model assumes lognormal distribution for large y. Rebuild static documentation. --- README.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'README.html') diff --git a/README.html b/README.html index c9f86c6b..f71dcfbc 100644 --- a/README.html +++ b/README.html @@ -155,7 +155,7 @@ $(document).ready(function () {
  • Summary and plotting functions. The summary of an mkinfit object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.
  • The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.
  • Iteratively reweighted least squares fitting is implemented in a similar way as in KinGUII and CAKE (see below). Simply add the argument reweight.method = "obs" to your call to mkinfit and a separate variance componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged.
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  • Iterative reweighting is also possible using the two-component error model for analytical data of Rocke and Lorenzato using the argument reweight.method = "tc".
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  • Iterative reweighting is also possible using a two-component error model for analytical data similar to the one proposed by Rocke and Lorenzato using the argument reweight.method = "tc".
  • When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.
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