Audit Gazepoint biometric synchronization drift — audit_gazepoint_biometric_sync

Combines time-order/reset diagnostics with conservative signal-lag summaries across signal pairs and groups. The helper is intended for quality control and synchronization review. It does not infer emotional valence, cognitive states, causal timing, or true physiological latency.

Usage

audit_gazepoint_biometric_sync_drift(
  data,
  time_col = NULL,
  group_cols = NULL,
  signal_pairs = NULL,
  signal_cols = NULL,
  reference_signal_col = NULL,
  max_lag = 1000,
  lag_step = NULL,
  drift_tolerance = NULL,
  method = c("pearson", "spearman"),
  min_complete_pairs = 20,
  use_first_difference = FALSE,
  include_reset_segments = TRUE
)

Arguments

data: A Gazepoint biometric data frame.
time_col: Optional time or counter column.
group_cols: Optional grouping columns.
signal_pairs: Optional two-column data frame, matrix, or list defining signal pairs. If NULL, pairs are formed between a reference signal and other detected biometric signals.
signal_cols: Optional candidate signal columns used when signal_pairs is NULL.
reference_signal_col: Optional reference signal used when signal_pairs is NULL.
max_lag: Maximum absolute lag to evaluate, in the same units as time_col.
lag_step: Step size between candidate lags. If NULL, the median positive time step is used.
drift_tolerance: Optional threshold for the range of estimated lags across groups. If NULL, drift is summarized but not threshold-classified.
method: Correlation method passed to stats::cor().
min_complete_pairs: Minimum complete aligned observations required for each candidate lag.
use_first_difference: If TRUE, lag diagnostics use first differences.
include_reset_segments: If TRUE, reset segments from audit_gazepoint_time_resets() are added to grouping when available.

Value

A list with overview, checks, time_reset_audit, lag_by_group, lag_profile, drift_summary, and settings.