Plot cluster-based permutation results — plot_gazepoint_cluster

Create a publication-ready time-course plot from the output of run_gazepoint_cluster_permutation(). The plot can show the mean condition difference, the time-wise test statistic, or both. Candidate time bins and cluster-level significant windows can be highlighted.

Usage

plot_gazepoint_cluster_results(
  result,
  plot_type = c("both", "difference", "statistic"),
  alpha = 0.05,
  significant_only = TRUE,
  show_clusters = TRUE,
  show_candidates = TRUE,
  show_threshold = TRUE,
  show_zero_line = TRUE,
  title = NULL,
  subtitle = NULL,
  x_label = "Time (ms)",
  y_label = NULL,
  line_width = 0.7,
  point_size = 1.8,
  cluster_alpha = 0.12
)

Arguments

result: A result object returned by run_gazepoint_cluster_permutation().
plot_type: Character. One of "both", "difference", or "statistic".
alpha: Cluster-level significance threshold used to decide which clusters are significant for plotting.
significant_only: Logical. If TRUE, only significant clusters are shaded. If FALSE, all observed clusters are shaded.
show_clusters: Logical. If TRUE, shade cluster windows.
show_candidates: Logical. If TRUE, mark time bins exceeding the cluster-forming threshold.
show_threshold: Logical. If TRUE, show the cluster-forming threshold on the statistic panel.
show_zero_line: Logical. If TRUE, add a horizontal zero reference line.
title: Optional plot title.
subtitle: Optional plot subtitle.
x_label: X-axis label.
y_label: Optional y-axis label. If NULL, a label is chosen automatically.
line_width: Width of the time-course line.
point_size: Size of candidate-bin points.
cluster_alpha: Transparency for shaded cluster windows.

Value

A ggplot object.

Details

Cluster-based permutation tests are intended for time-course inference. They should not be used to discover a confirmatory time window and then test that same window again in a second confirmatory model.