Hi,
In the data fitting area of Analysis we are offered error handling options of
'covariance','bootstrap' and 'jiggling'. Is there any description anywhere of
the relative merits or de-merits of these different options? Is any of them
similar or identical to using 'Monte Carlo' generation of data sets within the
uncertainties of the measured values (such is often used in some other
implementations and is a method that I intrinsically understand)?
Specifically, if I am trying to extract K_D for a ligand binding series what is
the best Analysis-based way of assessing the confidence limits on the fitted
values? (i.e. ideally I don't want to have to go to another program to sort this
out.)
[BTW, if you Google 'jiggling' you quickly run into material that can get one
fired from one's job; even 'jiggling fitting' does not help and I decided not to
go further.]
Thanks in advance,
Cheers,
Paul
--
Paul C. Driscoll
Honorary Professor of Structural Biology
Structural and Molecular Biology
University College London
Gower Street
London WC1E 6BT, UK
Tel.: 44-20 7679 7035
Mobile: 07876 777937
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