Summarize a probabilistic network.

Builds a per-node table describing the causal structure: each node's layer in the network, its parents, and the distribution it carries.

Usage

# S3 method for class 'prob_net'
summary(object, ...)

Arguments

object

An object of class "prob_net".

...

Additional arguments (not used).

Value

An object of class "summary.prob_net", a list with components:

n_nodes, n_edges, n_roots, n_terminals, depth

Structural counts. depth is the number of layers in the longest causal chain.

type_counts

A table of distribution types in use, or NULL.

missing_distributions

Character vector of node ids with no declared distribution.

node_table

Data frame with one row per node, in topological order, with columns id, label, group, layer, type, n_parents, parents and parameters. label and group are present only when the nodes data frame carries them.

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)
  )
)
summary(prob_net(nodes, links, distributions = dists))
#> Probabilistic Network of Project Risks
#> ------------------------------
#> Nodes: 2   Edges: 1   Roots: 1   Terminal: 1   Depth: 2 layers
#> 
#> Nodes (in topological order):
#>    id layer        type n_parents parents
#>  Risk     1    discrete         0        
#>  Task     2 conditional         1    Risk
#>                                parameters
#>                    discrete{0:0.7, 1:0.3}
#>  if Risk then normal(mean = 20, sd = 4...