cograph (version 2.7.2)

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 cograph_network object built by splot(), or NULL when no edge survives the significance filter. Called for the side effect of drawing.

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

# Mock a tna_permutation object with synthetic data
diffs <- matrix(c(0, .15, -.1, -.2, 0, .05, .1, -.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(.15,-.1,-.2,.05,.1,-.05),
    effect_size = c(2.1,-1.5,-2.8,.4,1.2,-.3),
    p_value     = c(.01,.04,.001,.3,.02,.5))))
attr(perm, "level")  <- 0.05
attr(perm, "labels") <- c("A", "B", "C")
class(perm) <- c("tna_permutation", "list")
plot_permutation(perm)