Audit Gazepoint pupil preprocessing imbalance

Check whether pupil preprocessing loss differs across experimental conditions or other grouping variables.

Usage

audit_gazepoint_pupil_imbalance(
  data,
  group_cols = "condition",
  pupil_col = "pupil_interpolated",
  interpolated_col = "pupil_was_interpolated",
  interpolation_status_col = "pupil_interpolation_status",
  artifact_col = NULL,
  artifact_reason_col = NULL,
  min_group_n = 1,
  max_valid_pct_diff = 10,
  max_artifact_pct_diff = 10,
  max_missing_pct_diff = 10,
  max_interpolated_pct_diff = 10
)

Arguments

data

A data frame from a pupil preprocessing pipeline.

group_cols

Character vector of grouping columns. By default, summaries are produced by condition.

pupil_col

Name of the post-preprocessing pupil column used to define remaining valid and missing samples.

interpolated_col

Name of the logical interpolation flag column.

interpolation_status_col

Name of the interpolation-status column.

artifact_col

Optional artifact flag column. If NULL, the function tries to detect pupil_artifact_flag, pupil_flag_invalid, or artifact_flag.

artifact_reason_col

Optional artifact-reason column. If NULL, the function tries to detect pupil_artifact_reason, pupil_flag_reason, or artifact_reason.

min_group_n

Minimum group size below which a group is flagged.

max_valid_pct_diff

Maximum acceptable range in valid-sample percentage across groups.

max_artifact_pct_diff

Maximum acceptable range in artifact percentage across groups.

max_missing_pct_diff

Maximum acceptable range in remaining-missing percentage across groups.

max_interpolated_pct_diff

Maximum acceptable range in interpolated percentage across groups.

Value

A tibble with one row per group and imbalance-warning columns.

Details

The function summarises valid pupil samples, interpolated samples, artifact-flagged samples, and remaining missing samples. It also adds simple imbalance flags based on differences between groups.