spey.multiparameter.contour.ContourResult#
- class spey.multiparameter.contour.ContourResult(theta_mle: ndarray, nll_min: float, threshold: float, delta: float, contour_points: ndarray, from_radial: ndarray, parameter_names: List[str] | None, confidence_level: float, dof: int)[source]#
Container for the output of
find_contour().- theta_mle#
Maximum-likelihood estimate \(\hat\theta\), shape
(k,).- Type:
np.ndarray
- nll_min#
Minimum negative log-likelihood \(\mathrm{NLL}(\hat\theta)\).
- Type:
float
- threshold#
NLL value that defines the contour boundary, \(T = \mathrm{NLL}(\hat\theta) + \Delta_\alpha / 2\).
- Type:
float
- delta#
Chi-squared quantile \(\Delta_\alpha = F^{-1}_{\chi^2_k}(1-\alpha)\).
- Type:
float
- contour_points#
Points on the contour boundary, shape
(n_points, k). Every row \(\theta^*\) satisfies \(|\mathrm{NLL}(\theta^*) - T| \lesssim \varepsilon_\text{tol}\).- Type:
np.ndarray
- from_radial#
Boolean mask of shape
(n_points,).Truefor points produced by the radial search;Falsefor points added by the constrained RATTLE walk.- Type:
np.ndarray
- parameter_names#
Names of the \(k\) parameters in the same order as the columns of
contour_points, orNonewhen the model does not provide names.- Type:
Optional[List[str]]
- confidence_level#
The confidence level \(1-\alpha\), e.g.
0.95.- Type:
float
- dof#
Degrees of freedom \(k\) (number of model parameters).
- Type:
int
- __init__(theta_mle: ndarray, nll_min: float, threshold: float, delta: float, contour_points: ndarray, from_radial: ndarray, parameter_names: List[str] | None, confidence_level: float, dof: int) None#
Methods
__init__(theta_mle, nll_min, threshold, ...)Attributes