Plot Event Model — plot.event_model • fmridesign

Creates a line plot visualization of the predicted BOLD response for each regressor in an event_model object.

Usage

# S3 method for class 'event_model'
plot(
  x,
  term_name = NULL,
  facet_threshold = Inf,
  label_mode = c("auto", "compact", "none"),
  max_labels = 30,
  abbrev_min = 10,
  strip_text_size = 8,
  ...
)

Arguments

x: An event_model object.
term_name: Character. Name of specific term to plot. If NULL, plots all terms.
facet_threshold: Integer. Switch to faceting when number of regressors exceeds this value. Default 6.
label_mode: Character. One of "auto", "compact", "none". In "auto" mode the method abbreviates labels for moderate counts and suppresses labels entirely when they are excessive (> max_labels). "compact" always abbreviates labels. "none" suppresses legend and facet strip labels.
max_labels: Integer. When label_mode = "auto" and the number of regressors exceeds this value, labels are suppressed. Default 30.
abbrev_min: Integer. Minimum length used by base::abbreviate() when compacting labels. Default 10.
strip_text_size: Numeric. Strip label text size when faceting with labels. Default 8.
...: Additional arguments (currently unused).

Value

A ggplot2 object showing the predicted BOLD timecourses.

Details

This method attempts to keep labels readable when there are many regressors (e.g., trial-wise designs) by switching to faceting and either abbreviating or suppressing labels depending on thresholds. You can control this behavior via label_mode, max_labels, and abbrev_min.

Examples

# Create a simple event model
des <- data.frame(
  onset = c(0, 10, 20, 30),
  run = 1,
  cond = factor(c("A", "B", "A", "B"))
)
sframe <- fmrihrf::sampling_frame(blocklens = 40, TR = 1)
emod <- event_model(onset ~ hrf(cond), data = des, block = ~run, sampling_frame = sframe)

# Plot all regressors
plot(emod)


# Plot specific term only
plot(emod, term_name = "cond")