What methods could be used to fit to this case?
jjweimer
How should I set up the fitting for this problem?
My first approach will be to fit each curve separately in order to get first-guess parameters for K, A, and ro. I would very much like to take advantage of the statistical improvements in K and A that would come from doing a combined fit on the N sets for these two parameters only, in the meantime allowing ro to vary.
I had an idea to concatenate the N data sets and create one general purpose fitting equation. The form of the fitting equation would end with N + 2 parameters (K, A, and ro[1 .. N]). I suspect this should work, though I hesitate to think about inconsistencies at boundaries or the enormity of the fitting equation as N gets large. Right now, N ~ 10.
Does a different and more elegant method exist to tackle such a problem?
You've just described global fitting. Any reason you can't simply use Igor's global fit package? (analysis -> packages -> global fit).
This is how the global fit package works. I'm not sure what you mean about inconsistencies at boundaries - the fitting function must know how the different datasets are grouped into the single bigger dataset. The global fit packages will take care of this for you.
April 14, 2013 at 10:08 am - Permalink
No reason other than ignorance.
Seems it is time to play with some tutorials.
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J. J. Weimer
Chemistry / Chemical & Materials Engineering, UAHuntsville
April 15, 2013 at 10:30 am - Permalink
John Weeks
WaveMetrics, Inc.
support@wavemetrics.com
April 15, 2013 at 09:50 am - Permalink