Hi All, I apologise if this question has been answered before, but my background is a little different from most people using R, and the language we use seems to be different! I am trying to analyse some nuclear physics data, which consists of an ensemble of "energy" readings in a detector that, when binned, form a number of Gaussian shaped peaks superimposed on a varying background (see attached pdf image).
In my field, the common practice is to bin these readings into a histogram, and then use a variety of chi-squared fitting techniques to fit a linear background and Gaussian directly to the binned counts to obtain a position and area under a peak. I understand that this is poor practice, especially given my rather low counting rate, so I have been attempting to fit the raw data with the fitdistrplus package (linked to from http://www.r-bloggers.com/fitting-distributions-with-r). I can fit an isolated Gaussian peak successfully using these methods (the central peak in my attached image). However, I cannot find a suitable distribution to account for the background in the peaks to the left of this (which I assume to vary linearly under each peak for now). Is this possible with usual distribution fitting methods, or should I stick to fitting the binned data (possibly including some interval censoring corrections)? Thank in advance for the help, Richard Longland
SamplePeak.pdf
Description: Adobe PDF document
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