Baseline-correct Gazepoint pupil data

Computes baseline-corrected pupil columns from a Gazepoint pupil time series, typically after flag_gazepoint_pupil() and interpolate_gazepoint_pupil(). Baselines can be defined either by a time window, such as c(-200, 0), or by a user-supplied logical baseline/pre-stimulus flag column.

Usage

baseline_correct_gazepoint_pupil(
  data,
  pupil_col = NULL,
  time_col = NULL,
  baseline_time_col = NULL,
  baseline_window = c(-200, 0),
  baseline_flag_col = NULL,
  group_cols = c("subject", "media_id"),
  baseline_method = c("mean", "median"),
  min_baseline_samples = 1
)

Arguments

data

A Gazepoint master table, preferably after interpolate_gazepoint_pupil().

pupil_col

Optional name of the pupil column to baseline-correct. If NULL, the function detects one of pupil_interpolated, pupil_for_preprocessing, mean_pupil, pupil, pupil_raw, left_pupil, or right_pupil.

time_col

Optional name of the main time column. If NULL, the function detects one of time_ms, time, time_orig, or time_orig_ms.

baseline_time_col

Optional name of the time column used for selecting baseline samples. If NULL, the function detects relative-time columns first, then falls back to time_col.

baseline_window

Numeric vector of length two giving the baseline window in milliseconds. Defaults to c(-200, 0). This can also be set to post-onset or early-window values such as c(0, 200) when no pre-stimulus period is available.

baseline_flag_col

Optional logical column identifying baseline samples. If supplied, this takes priority over baseline_window.

group_cols

Character vector of grouping columns used to compute one baseline per independent time series. Defaults to c("subject", "media_id"). Columns such as "trial" or "trial_global" can be added when available. Use character(0) for one global baseline.

baseline_method

Baseline statistic. One of "mean" or "median". Defaults to "mean".

min_baseline_samples

Minimum number of valid baseline samples required to compute a baseline. Defaults to 1.

Value

A tibble containing the original data plus baseline-correction columns.

Examples

if (FALSE) { # \dontrun{
flagged <- flag_gazepoint_pupil(master)
interpolated <- interpolate_gazepoint_pupil(flagged)

corrected <- baseline_correct_gazepoint_pupil(
  interpolated,
  baseline_window = c(-200, 0)
)

corrected <- baseline_correct_gazepoint_pupil(
  interpolated,
  baseline_window = c(0, 200)
)
} # }