Toolbox overview

_images/rsatoolbox_workflow.png

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 rsatoolbox.data.Dataset 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.