Plot a probabilistic network. — plot.prob

Draws the network as a layered directed graph: nodes are placed by their longest-path distance from a root, so causes sit above the effects they propagate into. Within-layer ordering is refined by barycenter sweeps to reduce edge crossings.

Usage

# S3 method for class 'prob_net'
plot(
  x,
  main = NULL,
  col = NULL,
  vertical = TRUE,
  node_cex = 3,
  label_cex = 0.7,
  ...
)

Arguments

x: An object of class "prob_net".
main: Optional plot title. If NULL, a default title is generated.
col: Node fill color or vector of colors. If NULL, nodes are colored by their group column when present, and uniformly otherwise.
vertical: Logical. If TRUE (default), layers run top to bottom; otherwise left to right.
node_cex: Node symbol size, passed to graphics::points().
label_cex: Node label size, passed to graphics::text().
...: Additional arguments passed to graphics::plot().

Value

Invisibly returns x.

Details

This is a readable layout for the small-to-moderate networks the module is designed for, implemented in base graphics so that no optional dependency is required. It does not route long edges around intervening layers the way a full Sugiyama implementation does. For large or dense graphs, pass the network to a dedicated graph package, for example igraph::graph_from_data_frame(x$links, vertices = x$nodes).

Examples

nodes <- data.frame(id = c("Risk", "Task"), stringsAsFactors = FALSE)
links <- data.frame(source = "Risk", target = "Task", stringsAsFactors = FALSE)
dists <- list(
  Risk = list(type = "discrete", values = c(0, 1), probs = c(0.7, 0.3)),
  Task = list(
    type = "conditional", condition = "Risk",
    true_dist = list(type = "normal", mean = 20, sd = 4),
    false_dist = list(type = "normal", mean = 10, sd = 2)
  )
)
plot(prob_net(nodes, links, distributions = dists))