emat/learn/linear_model.py
def coefficients_summary(self):
		"""
		A summary DataFrame of the coefficients.

		Includes coefficient estimates, standard error,
		t-stats and p values.

		Returns:
			pandas.DataFrame
		"""

		beta = pandas.DataFrame(
			data=self.coef_,
			index=self._Y_columns,
			columns=self._X_columns,
		)
		if self.fit_intercept:
			beta['_Intercept_'] = self.intercept_
		beta = beta.stack()
		beta.name = 'Coefficient'

		if self.stats_on_fit:
			se = pandas.DataFrame(
				data=self.stderr_,
				index=self._Y_columns,
				columns=self._X_columns,
			)
			if self.fit_intercept:
				se['_Intercept_'] = self.stderr_intercept_
			se = se.stack()
			se.name = 'StdError'

			t = pandas.DataFrame(
				data=self.t_,
				index=self._Y_columns,
				columns=self._X_columns,
			)
			if self.fit_intercept:
				t['_Intercept_'] = self.t_intercept_
			t = t.stack()
			t.name = 't-Statistic'

			p = pandas.DataFrame(
				data=self.p_,
				index=self._Y_columns,
				columns=self._X_columns,
			)
			if self.fit_intercept:
				p['_Intercept_'] = self.p_intercept_
			p = p.stack()
			p.name = 'p'

			return pandas.concat([beta, se, t, p], axis=1)

		# no stats on fit, just return beta
		return beta

		A summary DataFrame of the coefficients.

		Includes coefficient estimates, standard error,
		t-stats and p values.

		Returns:
			pandas.DataFrame
		
A summary DataFrame of the coefficients.
Includes coefficient estimates, standard error,
t-stats and p values.
A summary DataFrame of the coefficients . Includes coefficient estimates standard error t - stats and p values .
def coefficients_summary(self):
		beta = pandas.DataFrame(
			data=self.coef_,
			index=self._Y_columns,
			columns=self._X_columns,
		)
		if self.fit_intercept:
			beta['_Intercept_'] = self.intercept_
		beta = beta.stack()
		beta.name = 'Coefficient'
		if self.stats_on_fit:
			se = pandas.DataFrame(
				data=self.stderr_,
				index=self._Y_columns,
				columns=self._X_columns,
			)
			if self.fit_intercept:
				se['_Intercept_'] = self.stderr_intercept_
			se = se.stack()
			se.name = 'StdError'
			t = pandas.DataFrame(
				data=self.t_,
				index=self._Y_columns,
				columns=self._X_columns,
			)
			if self.fit_intercept:
				t['_Intercept_'] = self.t_intercept_
			t = t.stack()
			t.name = 't-Statistic'
			p = pandas.DataFrame(
				data=self.p_,
				index=self._Y_columns,
				columns=self._X_columns,
			)
			if self.fit_intercept:
				p['_Intercept_'] = self.p_intercept_
			p = p.stack()
			p.name = 'p'
			return pandas.concat([beta, se, t, p], axis=1)
		return beta
def coefficients_summary ( self ) : beta = pandas . DataFrame ( data = self . coef_ , index = self . _Y_columns , columns = self . _X_columns , ) if self . fit_intercept : beta [ '_Intercept_' ] = self . intercept_ beta = beta . stack ( ) beta . name = 'Coefficient' if self . stats_on_fit : se = pandas . DataFrame ( data = self . stderr_ , index = self . _Y_columns , columns = self . _X_columns , ) if self . fit_intercept : se [ '_Intercept_' ] = self . stderr_intercept_ se = se . stack ( ) se . name = 'StdError' t = pandas . DataFrame ( data = self . t_ , index = self . _Y_columns , columns = self . _X_columns , ) if self . fit_intercept : t [ '_Intercept_' ] = self . t_intercept_ t = t . stack ( ) t . name = 't-Statistic' p = pandas . DataFrame ( data = self . p_ , index = self . _Y_columns , columns = self . _X_columns , ) if self . fit_intercept : p [ '_Intercept_' ] = self . p_intercept_ p = p . stack ( ) p . name = 'p' return pandas . concat ( [ beta , se , t , p ] , axis = 1 ) return beta
A summary DataFrame of the coefficients.
A summary DataFrame of the coefficients .
"""
		A summary DataFrame of the coefficients.

		Includes coefficient estimates, standard error,
		t-stats and p values.

		Returns:
			pandas.DataFrame
		""" # no stats on fit, just return beta
None
self