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authorJohannes Ranke <jranke@uni-bremen.de>2018-07-18 15:18:30 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2018-07-18 15:58:46 +0200
commit0b98c459c30a0629a728acf6b311de035c55fb64 (patch)
treef146faf4802da38862aa14b0268265f3fad9ba34 /README.html
parentd3ed95f2a0a43ed74b02ea90e35d043ed4e1e72f (diff)
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.
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<li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li>
<li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li>
<li>Iteratively reweighted least squares fitting is implemented in a similar way as in KinGUII and CAKE (see below). Simply add the argument <code>reweight.method = &quot;obs&quot;</code> to your call to <code>mkinfit</code> 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.</li>
-<li>Iterative reweighting is also possible using the two-component error model for analytical data of <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_rl.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = &quot;tc&quot;</code>.</li>
+<li>Iterative reweighting is also possible using a two-component error model for analytical data similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = &quot;tc&quot;</code>.</li>
<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li>
</ul>
</div>

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