plot.Disco: Plot a Disco Object

Description

Visualize a causal graph stored within a Disco object. This function extends plot.Knowledge() by combining the causal graph from a caugi object with background knowledge.

Usage

# S3 method for Disco
plot(x, required_col = "blue", ...)

Value

Invisibly returns the underlying caugi object. The main effect is the plot.

Arguments

x: A Disco object containing both the causal graph and the associated knowledge.
required_col: Character(1). Color for edges marked as "required". Default "blue".
...: Additional arguments passed to caugi::plot() and plot.Knowledge().

Details

Required edges are drawn in blue by default (required_col), can be changed.
Forbidden edges are not drawn by.
If tiered knowledge is provided, nodes are arranged according to their tiers.
Other edge styling (line width, arrow size, etc.) can be supplied via edge_style. To override the color of a specific edge, specify it in edge_style$by_edge[[from]][[to]]$col.

This function combines the causal graph and the Knowledge object into a single plotting structure. If the knowledge contains tiers, nodes are laid out accordingly; otherwise, the default caugi layout is used. Edges marked as required are automatically colored (or can be overridden per edge using edge_style$by_edge).

Examples

Run this code

data(tpc_example)

# Define tiered knowledge
kn <- knowledge(
  tpc_example,
  tier(
    child ~ starts_with("child"),
    youth ~ starts_with("youth"),
    old ~ starts_with("old")
  )
)

# Fit a causal discovery model
cd_tges <- tges(engine = "causalDisco", score = "tbic")
disco_cd_tges <- disco(data = tpc_example, method = cd_tges, knowledge = kn)

# Plot with default column orientation
plot(disco_cd_tges)

# Plot with row orientation
plot(disco_cd_tges, orientation = "rows")

# Plot with custom node and edge styling
plot(
  disco_cd_tges,
  node_style = list(
    fill = "lightblue", # Fill color
    col = "darkblue", # Border color
    lwd = 2, # Border width
    padding = 4, # Text padding (mm)
    size = 1.2 # Size multiplier
  ),
  edge_style = list(
    lwd = 1.5, # Edge width
    arrow_size = 4, # Arrow size (mm)
    col = "darkgreen", # Edge color
    fill = "black", # Arrow fill color
    lty = "dashed" # Edge line type
  )
)

# To override a specific edge style which is required you need to target that individual node:
kn <- knowledge(
  tpc_example,
  tier(
    child ~ starts_with("child"),
    youth ~ starts_with("youth"),
    old ~ starts_with("old")
  ),
  child_x1 %-->% c(child_x2, youth_x4) # required edges
)
bnlearn_pc <- pc(engine = "bnlearn", test = "fisher_z")
disco_bnlearn_pc <- disco(data = tpc_example, method = bnlearn_pc, knowledge = kn)

# Edge from child_x1 to child_x2 will be orange, but edge from child_x1 to youth_x4
# will be required_col (blue) since we only override the child_x1 to child_x2 edge.
plot(
  disco_bnlearn_pc,
  edge_style = list(
    by_edge = list(
      child_x1 = list(
        child_x2 = list(col = "orange", fill = "orange")
      )
    )
  ),
  required_col = "blue"
)

# Plot without tiers
data(num_data)
kn_untiered <- knowledge(
  num_data,
  X1 %-->% c(X2, X3),
  Z %!-->% Y
)

bnlearn_pc <- pc(engine = "bnlearn", test = "fisher_z")
res_untiered <- disco(data = num_data, method = bnlearn_pc, knowledge = kn_untiered)
plot(res_untiered)

# With a custom defined layout
custom_layout <- data.frame(
 name = c("X1", "X2", "X3", "Z", "Y"),
 x = c(0, 1, 2, 2, 3),
 y = c(0, 1, 0.25, -1, 0)
)
plot(res_untiered, layout = custom_layout)