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Ardo Illaste edited fit_iter.md
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####Iterative fitting
Fitting of potential event regions is performed iteratively. The algorithm is depicted on figure x.
First, the highest ranked regions are fitted with the extended fit function. After parameter optimization with the signal in the region is fitted with a linear model. For both models the corrected Akaike Information Criterion (AICc \cite{Burnham_2004}) is calculated and the region is taken to contain an event only if the AICc for the fit function is less than the AICc for the line. This ensures that the fit obtained with the fit function is good enough to justify the use of a more complicated model. After this each the fits are subtracted from the original signal. This allows the lower ranked regions to be fitted with reduced interference from higher ranking regions (see supporting info figures). When all regions have been fitted and their fits subtracted from the signal, the remaining signal is fitted with a polynomial function to approximate the baseline fluorescence.
At each pass regions which have no overlaps and regions which only overlap with regions having a lower peak SNR are fitted (Figure \ref{fig:regs}D). After a successful fit, the result is subtracted from the original signal and region detection is performed again, followed by fitting. This process is continued until no more regions are detected or successfully fitted.
