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Grand-average event-related skin conductance response (ER-SCR)

Source: R/eda-plot.R
plotErscrGrandAverage.Rd

Computes and plots the grand-average phasic skin-conductance waveform time-locked to stimulus events, separately per event type (condition), with a confidence-interval band. Each event's epoch over window is baseline corrected against the pre-event baseline interval, epochs are averaged within a condition, and the band is the standard error of the mean scaled to ci_level (Boucsein 2012; Bach event-related SCR averaging). The epoching reuses the event onsets from PhysioCore::getEvents() on the same sample grid as edaErscr().

Usage

plotErscrGrandAverage(
  x,
  event_types = NULL,
  window = c(-1, 6),
  baseline = c(-1, 0),
  channel = 1,
  assay_name = NULL,
  ci = c("sem", "none"),
  ci_level = 0.95,
  colors = NULL,
  plot = TRUE,
  ...
)

Arguments

x

A PhysioExperiment object with a skin conductance assay and events.

event_types

Character vector of event types to average and overlay; NULL (default) uses every event type present.

window

Numeric c(pre, post) epoch limits in seconds relative to the event onset (default c(-1, 6)).

baseline

Numeric c(start, end) pre-event baseline interval in seconds subtracted from each epoch, or NULL for no baseline correction (default c(-1, 0)).

channel

Channel index to average (default 1).

assay_name

Input assay (default: defaultAssay(x)).

ci

Confidence band: "sem" (mean +/- z * SEM, default) or "none".

ci_level

Confidence level for the SEM band (default 0.95).

colors

Optional per-condition colours (recycled).

plot

Draw the plot (default TRUE); set FALSE to only compute.

...

Additional arguments passed to plot().

Value

Invisibly, an erscr_grand_average object with grand_average (a tidy data frame of type, time, mean, sd, sem, ci_lower, ci_upper, n_trials), the per-condition baseline-corrected epochs matrices, and the time axis.

References

Boucsein, W. (2012). Electrodermal Activity. 2nd ed. Springer.

See also

edaErscr()

Examples

x <- make_eda_with_scr()
ga <- plotErscrGrandAverage(x, plot = FALSE)
head(ga$grand_average)
#>                type time          mean          sd         sem     ci_lower
#> stimulus.1 stimulus -1.0  0.0276643418 0.035345176 0.017672588 -0.006973294
#> stimulus.2 stimulus -0.9  0.0188938019 0.033667749 0.016833875 -0.014099986
#> stimulus.3 stimulus -0.8  0.0138053383 0.023610669 0.011805335 -0.009332692
#> stimulus.4 stimulus -0.7  0.0078882259 0.013536197 0.006768099 -0.005377004
#> stimulus.5 stimulus -0.6 -0.0034103585 0.009070294 0.004535147 -0.012299083
#> stimulus.6 stimulus -0.5  0.0002261251 0.007947753 0.003973877 -0.007562530
#>               ci_upper n_trials
#> stimulus.1 0.062301978        4
#> stimulus.2 0.051887590        4
#> stimulus.3 0.036943369        4
#> stimulus.4 0.021153456        4
#> stimulus.5 0.005478366        4
#> stimulus.6 0.008014780        4