PSILOGIT.Methods.Sigle.Sigle

class Sigle

Class implementing the Post-Selection Inference procedure proposed with the SIGLE method in both the selected and the saturated model.

__init__()

Methods

__init__()
compute_selection_event([...])	Finds all the vectors belonging to the selection event.
compute_theta_bar(barpi[, grad_descent])	Computes \(\overline \theta(\theta^*) \in \mathbb R^s\) which is the unique vector satisfying \(\mathbf X_M^{\top}\sigma(\mathbf X_M \overline \theta (\theta^*))=\mathbf X_M^{\top} \overline \pi^{\theta^*}\) where \(\overline \pi^{\theta^*}\) is the input parameter 'barpi'.
ellipse_testing(states, barpi[, signull, ...])	For a selected support of size 2, this method show the proportion of stats following in the ellipse characterizing the rejection rejection of the hypothesis test with SIGLE in the selected model.
histo_time_in_selection_event(states, ...[, ...])	Histogram showing the time spent in the selection event using the SEI-SLR algorithm.
last_visited_states(states[, ...])	Shows that time spent in the selection event using the SEI-SLR algorithm.
params_saturated(bern, states)	Computes the probability of the vector of bits 'z' when the expected value of the response vector is given by 'bern'
pval_SIGLE(states, barpi[, net, ...])	Computes the P-values using the post-selection inference method SIGLE (both in the saturated and the selected model).
time_in_selection_event(states, ...[, fig_name])	Shows that time spent in the selection event using the SEI-SLR algorithm for several different excursions.
upper_bound_condition_CCLT(states, barpi, ...)	Computes quantities arising in the assumption of our conditional Central Limit Theorem.