Parameters: |
- inst : instance of Epochs | iterable of SourceEstimate
The data to be regressed. Contains all the trials, sensors, and time
points for the regression. For Source Estimates, accepts either a list
or a generator object.
- design_matrix : ndarray, shape (n_observations, n_regressors)
The regressors to be used. Must be a 2d array with as many rows as
the first dimension of data. The first column of this matrix will
typically consist of ones (intercept column).
- names : list-like | None
Optional parameter to name the regressors. If provided, the length must
correspond to the number of columns present in regressors
(including the intercept, if present).
Otherwise the default names are x0, x1, x2…xn for n regressors.
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Returns: |
- results : dict of namedtuple
For each regressor (key) a namedtuple is provided with the
following attributes:
beta : regression coefficients
stderr : standard error of regression coefficients
t_val : t statistics (beta / stderr)
p_val : two-sided p-value of t statistic under the t distribution
mlog10_p_val : -log10 transformed p-value.
The tuple members are numpy arrays. The shape of each numpy array is
the shape of the data minus the first dimension; e.g., if the shape of
the original data was (n_observations, n_channels, n_timepoints),
then the shape of each of the arrays will be
(n_channels, n_timepoints).
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