There may be situations where you would like to fit multiple
datasets with the same fitting function. It may also be the case
that the datasets you wish to fit share one or more parameters in
the common fitting function. In Origin, we call this type of
fitting operation Global Fitting, borrowing from general
programming lexicon in which a global variable is one that
can be seen by all subroutines, functions, classes, and
Origin supports global fitting wherein each parameter in the
common fitting function can optionally be shared by all datasets,
or can be kept independent. If a parameter is shared, the fitting
procedure will yield one best-fit value for that parameter for
all datasets. If a parameter is not shared, the fitting procedure
will yield a unique value for each dataset.
Global Fitting in Origin, key points:
- Multiple datasets are fitted with one model
- Fit parameters are optionally shared between the datasets.
- For each shared (global) parameter, one best-fit value
is estimated from all of the datasets that are fitted.
- For each non-shared (local) parameter, a unique
best-fit value is generated for individual dataset that is
- Other options such as constraints and weighting are also
available when you choose to perform Global