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After loading we may assign different priorities of the datasets for this global analysis. The priority is a multiplier applied to the weighted, averaged sum of squares from the dataset prior to adding to the total sum of squares. Normally, these priorities are set to 1. Equal priorities are most appropriate for the datasets of the same type, because relative errors of points and number of points in the datasets are already accounted for in calculation of the sum of squares from the dataset. However, when analyzing datasets of different types we may want to emphasize some data at the expense of the others. This is completely arbitrary decision because, again, relative errors and sizes of datasets are already taken into account. Yet, if we have 10 datasets of one type and 1 dataset of another type it may be useful to set priority of the former 10 datasets to 0.1 to force analysis 'equally' satisfy both types of data. This decision is up to the user and must be reported together with the results of fitting as priorities are designed to bias best-fit parameters. IMPORTANT NOTE: I have not proven yet that statistical hypothesis testing using Akaike's Information Criterion is compatible with use of priorities. Therefore, if you need statistical testing of your models - don't use priorities!


For specific usage see TotalFit.m