BorgDiagnosis S4 Class

Holds the result of borg_diagnose: a structured assessment of data dependency patterns that affect cross-validation validity.

Usage

# S4 method for class 'BorgDiagnosis'
show(object)

dependency_type: Character. Primary dependency type detected: "none", "spatial", "temporal", "clustered", or "mixed".
severity: Character. Overall severity: "none", "moderate", "severe".
recommended_cv: Character. Recommended CV strategy: "random", "spatial_block", "temporal_block", "group_fold", "spatial_temporal".
spatial: List. Spatial autocorrelation diagnostics with elements: detected (logical), morans_i (numeric), morans_p (numeric), range_estimate (numeric), effective_n (numeric), coords_used (character).
temporal: List. Temporal autocorrelation diagnostics with elements: detected (logical), acf_lag1 (numeric), ljung_box_p (numeric), decorrelation_lag (integer), embargo_minimum (integer), time_col (character).
clustered: List. Clustered structure diagnostics with elements: detected (logical), icc (numeric), n_clusters (integer), cluster_sizes (numeric), design_effect (numeric), group_col (character).
inflation_estimate: List. Estimated metric inflation from random CV with elements: auc_inflation (numeric, proportion), rmse_deflation (numeric), confidence (character: "low"/"medium"/"high"), basis (character).
n_obs: Integer. Number of observations in the dataset.
timestamp: POSIXct. When the diagnosis was performed.
call: Language object. The original call that triggered diagnosis.