ConditionalSpectralCausalityResult._summary_table#

ConditionalSpectralCausalityResult._summary_table()[source]#

Build the structured summary: one row per effect, largest first.

The integrated measure of the cause on each effect, sorted descending, under a header with the cause, the effect count, and the grid; the notes say what the measure is, report the consistency diagnostic, and restate that the curves describe the fitted models.

Return type:

SummaryTable

Returns:

The SummaryTable that summary(), str(), and the notebook renderer display.

Example

>>> import numpy as np
>>> from cultivars.multivariate.reduced_form.vector_autoregression import VAR
>>> y = np.random.default_rng(0).standard_normal((200, 3))
>>> full = VAR(y, order=1).fit()
>>> without = VAR(y[:, [0, 2]], order=1, names=("y1", "y3")).fit()
>>> table = ConditionalSpectralCausality(full, without).compute()._summary_table()
>>> table.columns, len(table.rows), table.metadata[0]
(('pair', 'integrated measure'), 2, ('Cause', 'y2'))