Harvest Diagnostics from a Fitted EventStudyTask

Extracts already-computed statistical signals from a fitted EventStudyTask into a flat, JSON-ready S3 list. This is the always-available grounding foundation for the knowledge base and offline advice layer — it recomputes nothing; all signals already exist in the fitted task.

Usage

es_diagnostics(task, max_events = 20L)

Arguments

task

A fitted EventStudyTask (after fit_model() and optionally calculate_statistics() have been called).

max_events

Integer. Maximum number of events to include in the per-event sections (estimation_window, event_window, contract_state). Events are ranked by anomaly score (degenerate events first, then by absolute final CAR). The remainder is summarised in aggregate_summary. Default: 20.

Value

A named list of class "es_diagnostics" with six sections:

meta

List: n_events_total (integer), n_events_shown (integer), n_events_summarized (integer), event_ids_shown (integer vector).

estimation_window

List of plain numeric vectors (length n_events_shown): r2, sigma, degree_of_freedom, shapiro_p, dw_stat, ljung_box_p, acf1. Each entry is NA_real_ when the model was not fitted or residuals are insufficient for the test.

event_window

List of plain numeric vectors (length n_events_shown): ar_t (last event-window day AR t-stat), ar_p (two-sided p-value), car_t (full-window CAR t-stat), car_p (two-sided p-value), final_car. All NA_real_ when the ART/CART columns are absent.

cross_sectional

List of scalars aggregated across ALL events: n_events (total), n_valid_events (fitted count), car_iqr, car_sd, n_overlap_pairs, any_overlap. Multi-event fields degrade to NA when task$aar_caar_tbl is NULL.

contract_state

List of logical/numeric vectors (length n_events_shown): is_fitted, na_ar_count, na_est_count, insufficient_obs, zero_var_index.

aggregate_summary

Named list summarising remainder events (those beyond max_events): n_summarized, mean_r2, median_r2, mean_final_car, n_fitted, n_degenerate. NULL when no events are summarised (all shown).

See also

model_diagnostics, recommend_stat, flag_robustness