AsymptoticLimits functions give back LLR rather than -2 LLR
@@ -210,11 +210,11 @@ class AsymptoticLimits(DataAnalyzer):
mle : int
Index of the parameter point with the best fit (largest p-value / smallest -2 log likelihood ratio).
- nllr_kin : ndarray or None
- -2 log likelihood ratio based only on kinematics for each point of the grid, with shape `(n_grid_points,)`.
+ log_likelihood_ratio_kin : ndarray or None
+ log likelihood ratio based only on kinematics for each point of the grid, with shape `(n_grid_points,)`.
- nll_rate : ndarray or None
- -2 log likelihood based only on the total rate for each point of the grid, with shape `(n_grid_points,)`.
+ log_likelihood_rate : ndarray or None
+ log likelihood based only on the total rate for each point of the grid, with shape `(n_grid_points,)`.
histos : None or list of Histogram
None if return_histos is False. Otherwise a list of histogram objects for each point on the grid. This
@@ -404,11 +404,11 @@ class AsymptoticLimits(DataAnalyzer):
mle : int
Index of the parameter point with the best fit (largest p-value / smallest -2 log likelihood ratio).
- nllr_kin : ndarray or None
- -2 log likelihood ratio based only on kinematics for each point of the grid, with shape `(n_grid_points,)`.
+ log_likelihood_ratio_kin : ndarray or None
+ log likelihood ratio based only on kinematics for each point of the grid, with shape `(n_grid_points,)`.
- nll_rate : ndarray or None
- -2 log likelihood based only on the total rate for each point of the grid, with shape `(n_grid_points,)`.
+ log_likelihood_rate : ndarray or None
+ log likelihood based only on the total rate for each point of the grid, with shape `(n_grid_points,)`.
histos : None or list of Histogram
None if return_histos is False. Otherwise a list of histogram objects for each point on the grid. This
@@ -623,7 +623,7 @@ class AsymptoticLimits(DataAnalyzer):
logger.debug("Min-subtracted -2 log r: %s", -2.0 * log_r)
p_values = self.asymptotic_p_value(log_r, dof=dof)
- return theta_grid, p_values, i_ml, -2.0 * log_r_kin, -2.0 * log_p_xsec, histos if return_histos else None
+ return theta_grid, p_values, i_ml, log_r_kin, log_p_xsec, histos if return_histos else None
def _find_bins(self, mode, hist_bins, summary_stats):
n_summary_stats = summary_stats.shape[1]
