cograph (version 2.0.0)

splot.tna_permutation: Plot Permutation Test Results

Description

Visualizes permutation test results with styling to distinguish significant from non-significant edge differences. Works with tna_permutation objects from the tna package.

Usage

splot.tna_permutation(x, ...)
plot_permutation(
  x,
  show_nonsig = FALSE,
  edge_positive_color = "#009900",
  edge_negative_color = "#C62828",
  edge_nonsig_color = "#888888",
  edge_nonsig_style = 2,
  show_stars = TRUE,
  show_effect = FALSE,
  edge_nonsig_alpha = 0.4,
  ...
)

Value

Invisibly returns the plot.

Arguments

x

A tna_permutation object (from tna::permutation_test).

...

Additional arguments passed to splot().

show_nonsig

Logical: show non-significant edges? Default FALSE (only significant shown).

edge_positive_color

Color for positive differences (x > y). Default "#009900" (green).

edge_negative_color

Color for negative differences (x < y). Default "#C62828" (red).

edge_nonsig_color

Color for non-significant edges. Default "#888888" (grey).

edge_nonsig_style

Line style for non-significant edges (2=dashed). Default 2.

show_stars

Logical: show significance stars (*, **, ***) on edges? Default TRUE.

show_effect

Logical: show effect size in parentheses for significant edges? Default FALSE.

edge_nonsig_alpha

Alpha for non-significant edges. Default 0.4.

Details

The function expects a tna_permutation object containing:

• edges$diffs_true: Matrix of actual edge differences (x - y)

• edges$diffs_sig: Matrix of significant differences only

• edges$stats: Data frame with edge_name, diff_true, effect_size, p_value

Edge styling:

• Significant positive: solid green, bold labels with stars

• Significant negative: solid red, bold labels with stars

• Non-significant (when show_nonsig=TRUE): dashed grey, plain labels, lower alpha

Examples

if (FALSE) {
# Create a mock tna_permutation object with synthetic data
set.seed(42)
diffs <- matrix(c(0, 0.15, -0.1, -0.2, 0, 0.05, 0.1, -0.05, 0), 3, 3)
rownames(diffs) <- colnames(diffs) <- c("A", "B", "C")
diffs_sig <- diffs
diffs_sig[abs(diffs) < 0.1] <- 0
perm <- list(
  edges = list(
    diffs_true = diffs,
    diffs_sig = diffs_sig,
    stats = data.frame(
      edge_name = c("A -> B", "A -> C", "B -> A", "B -> C", "C -> A", "C -> B"),
      diff_true = c(0.15, -0.1, -0.2, 0.05, 0.1, -0.05),
      effect_size = c(2.1, -1.5, -2.8, 0.4, 1.2, -0.3),
      p_value = c(0.01, 0.04, 0.001, 0.3, 0.02, 0.5)
    )
  )
)
attr(perm, "level") <- 0.05
attr(perm, "labels") <- c("A", "B", "C")
class(perm) <- c("tna_permutation", "list")
plot_permutation(perm)
}