Hi Jenny, I don't know what exactly you are trying to do so I can't say which method is better but I can make a comment. It all comes down to the number of independent variables. The Gaussian and step functions are independent of each other. The heights, widths and positions can all be adjusted. In the linear combination you change everything together so for example, the ratio of the peak height and the step height doesn't change. The result is that you need more spectra to get the same number of degrees of freedom as in the least-squares peak fit. Cheers, Adam Jenny Cai wrote:
Hello everyone,
Sorry for bothering you again.
I am using least-squares peak fit and linear combination fit to analyze my samples. I have spent tons of time on it, and it really makes me crazy. Why can't I get consistent results from these two methods?
Please see the attached file. Both of these methods work well individually, but linear combination fit always need more peaks than peak fit to get an 'ok' fitting. Should I stick on one method for all my samples, no matter what results the other one gives? It is confusing me so much. Could anyone help me out?
Thank you in advance for your help!
Jenny