rsatoolbox.inference.result module¶
Result object definition
- class rsatoolbox.inference.result.Result(models, evaluations, method, cv_method, noise_ceiling, variances=None, dof=1, fitter=None, n_rdm=None, n_pattern=None)[source]¶
Bases:
object
Result class storing results for a set of models with the models, the results matrix and the noise ceiling
- Parameters:
models (list of rsatoolbox.model.Model) – the evaluated models
evaluations (numpy.ndarray) – evaluations of the models over bootstrap/crossvalidation format: bootstrap_samples x models x crossval & others such that np.mean(evaluations[i,j]) is a valid evaluation for the jth model on the ith bootstrap-sample
method (String) – the evaluation method
cv_method (String) – crossvalidation specification
noise_ceiling (numpy.ndarray) – noise ceiling such that np.mean(noise_ceiling[0]) is the lower bound and np.mean(noise_ceiling[1]) is the higher one.
- Variables:
inputs (as) –
- get_errorbars(eb_type='sem', test_type='t-test')[source]¶
returns errorbars for the model evaluations
- save(filename, file_type='hdf5', overwrite=False)[source]¶
saves the results into a file.
- Parameters:
filename (String) – path to the file [or opened file]
file_type (String) – Type of file to create: hdf5: hdf5 file pkl: pickle file
overwrite (Boolean) – overwrites file if it already exists
- summary(test_type='t-test')[source]¶
Human readable summary of the results
- Parameters:
test_type (String) – What kind of tests to run. See rsatoolbox.util.inference_util.all_tests for options
- test_all(test_type='t-test')[source]¶
returns all p-values: p_pairwise, p_zero & p_noise
- Parameters:
test_type (String) – What kind of tests to run. See rsatoolbox.util.inference_util.all_tests for options