Toolbox overview


Overview over subpackages and work flow in rsatoolbox.

The Figure above shows the most important subpackages (blue), classes (gray), modules (yellow) and auxillary materials (orange) of the RSA toolbox. A common use of the RSA toolbox involves the following steps:

  • Extract the data that you want to analyzed. The data is stored in the format of a object, see Defining the data set.

  • Use functions from the module rsatoolbox.rdm.calc to calculate a RDM from the data, with many options for different dissimilarity measures, see Estimating dissimilarities.

  • Define RSA models by defining objects of the rsatoolbox.model class. For information on the different model types, see Model Specification.

  • Models can be fitted to the data and then evaluated using the rsatoolbox.inference.evaluate module. Results of the evaluation is stored in a rsatoolbox.inference.results object.

  • Dataset, RDMs, Models, and results can be visualized using the rsatoolbox.vis subpackage.

  • For simulation of artificial data sets, you can used the rsatoolbox.sim.simulation module.

For an example of a complete workflow, see the “getting started with RSAtoolbox” Notebook, Demos.