Skip to contents

Probabilistic Network of Project Risks.

Source: R/network.R
prob_net.Rd

This function is part of the probabilistic network module, whose API may still evolve in future versions.

Usage

prob_net(nodes, links, distributions = NULL)

Arguments

nodes

A data frame containing the nodes of the graph. Must include a column id with unique identifiers for each node.

A data frame containing the links of the graph. Must include columns source and target specifying the nodes that form each edge.

distributions

A named list where names correspond to node IDs and values specify discrete probabilities, continuous probability distributions, conditional distributions, or aggregate distributions.

  • "discrete": Specifies values and probs.

  • "normal": Specifies mean and sd.

  • "lognormal": Specifies meanlog and sdlog.

  • "uniform": Specifies min and max.

  • "conditional": Specifies a condition (a discrete or conditional node) and two distributions (true_dist, false_dist). The conditional distributions can themselves be discrete or continuous.

  • "aggregate": Specifies nodes (a list of continuous node IDs to sum).

Value

An S3 object of class "prob_net": a list with

  • nodes: The input nodes data frame.

  • links: The input links data frame.

  • adjacency_matrix: A directed matrix with a 1 in [source, target] for every edge.

  • distributions: The input distributions list.

Objects of this class have print.prob_net(), summary.prob_net() and plot.prob_net() methods.

Details

This function creates a probabilistic network graph representation of project risks that supports discrete and continuous probability distributions.

The links are load-bearing. A conditional node depends on its condition and an aggregate node depends on every node it sums, and prob_net() requires the edges in links to match that declared structure exactly: an edge with no corresponding dependency, or a dependency with no corresponding edge, is an error rather than a silently ignored inconsistency. Nodes must additionally be supplied in a topological order, with every link running from an earlier node to a later one, which is the order prob_net_sim() samples in and which also guarantees the graph is acyclic.

Examples

nodes <- data.frame(id = c("A", "B", "C", "D"))
links <- data.frame(
  source = c("A", "B", "C"),
  target = c("B", "D", "D")
)
distributions <- list(
  A = list(type = "discrete", values = c(1, 0), probs = c(0.5, 0.5)),
  B = list(
    type = "conditional", condition = "A",
    true_dist  = list(type = "normal", mean = 1, sd = 0.5),
    false_dist = list(type = "lognormal", meanlog = -1, sdlog = 0.5)
  ),
  C = list(type = "uniform", min = 1, max = 5),
  D = list(type = "aggregate", nodes = c("B", "C"))
)
graph <- prob_net(nodes, links, distributions = distributions)