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Plot Bootstrap Results

Source: R/plot-bootstrap.R
splot.tna_bootstrap.Rd

Visualizes bootstrap analysis results with styling to distinguish significant from non-significant edges. Works with tna_bootstrap objects from the tna package.

Usage

# S3 method for class 'tna_bootstrap'
plot(x, ...)

splot.tna_bootstrap(
  x,
  display = c("styled", "significant", "full", "ci"),
  edge_style_sig = 1,
  edge_style_nonsig = 2,
  color_nonsig = "#888888",
  show_ci = FALSE,
  show_stars = TRUE,
  width_by = NULL,
  inherit_style = TRUE,
  ...
)

Arguments

x

A tna_bootstrap object (from tna::bootstrap).

...

Additional arguments passed to splot().

display

Display mode:

  • "styled" (default): All edges with styling to distinguish significant/non-significant

  • "significant": Only significant edges

  • "full": All edges without significance styling

  • "ci": Show CI bands on edges

edge_style_sig

Line style for significant edges (1=solid). Default 1.

edge_style_nonsig

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

color_nonsig

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

show_ci

Logical: overlay CI bands on edges? Default FALSE.

show_stars

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

width_by

Optional: "cr_lower" to scale edge width by lower consistency range bound.

inherit_style

Logical: inherit colors/layout from original TNA model? Default TRUE.

Value

Invisibly returns the plot.

Details

The function expects a tna_bootstrap object containing:

  • weights or weights_orig: Original weight matrix

  • weights_sig: Significant weights only (optional)

  • p_values: P-value matrix

  • ci_lower, ci_upper: Confidence interval bounds

  • level: Significance level (default 0.05)

  • model: Original TNA model for styling inheritance

Edge styling in "styled" mode:

  • Significant edges: solid dark blue, bold labels with stars, rendered on top

  • Non-significant edges: dashed pink, plain labels, rendered behind

Examples

# Mock a tna_bootstrap object with synthetic data
w <- matrix(c(0, .3, .1, .2, 0, .4, .3, .1, 0), 3, 3)
rownames(w) <- colnames(w) <- c("A", "B", "C")
p <- matrix(c(1, .01, .5, .03, 1, .001, .2, .8, 1), 3, 3)
boot <- list(weights = w, p_values = p,
             ci_lower = w - 0.05, ci_upper = w + 0.05, level = 0.05,
             model = list(weights = w, labels = c("A", "B", "C")))
class(boot) <- c("tna_bootstrap", "list")
splot(boot)

splot(boot, display = "significant")