fit_within_beh

class mne_plsc.fit_within_beh(data, covariates, within=None, source_domain=None, source_freqs=None, boot_stat='score-covariate-corr', svd_method='lapack', random_state=None)

Fit within-participants behaviour PLS model.

Parameters:

• data (iterable of MNE objects) – The M/EEG data to analyze. Each element of the iterable should correspond to a single participant.

• covariates (iterable of str) – An iterable of strings specifying the name(s) of the columns in the .metadata of each object in data that contain the covariates.

• within (str, optional) – A string specifying the name of a column in the .metadata of each object in data that contains an indicator of within-participants condition. The default is None, which does not stratify observations by within-participants condition.

• source_domain (str, optional) –

  If model is fit to source-space data, this argument specifies the domain of the source space. Must be one of:

  • 'time' For output of mne.minimum_norm.apply_inverse(), mne.beamformer.apply_lcmv(), etc. This is assumed by default.

  • 'freq' For output of mne.minimum_norm.apply_inverse_cov(), mne.beamformer.apply_dics(), etc.

  • 'time-freq' For output of mne.minimum_norm.apply_dics_tfr_epochs, :func:`mne.beamformer.apply_dics_tfr_epochs(), etc.

• source_freqs (numpy.ndarray, optional) – If model is fit to source-space data and source domain is time or time-frequency, this argument specifies the frequencies in the source data.

• boot_stat (str, optional) – Specifies which statistic should be computed on each bootstrap iteration. The default is 'score-covariate-corr'.

• svd_method (str, optional) – The method of SVD decomposition. The default is 'lapack'.

• random_state (int, optional) – Random state for seeding the model. The default is None.

Returns:

PLSC model fit to the data.

Return type:

PLSC