This function creates a probabilistic network graph representation of project risks that supports discrete and continuous probability distributions.
Arguments
- nodes
A data frame containing the nodes of the graph. Must include a column `id` with unique identifiers for each node.
- links
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 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
A list with: - `nodes`: The input `nodes` data frame. - `links`: The input `links` data frame. - `adjacency_matrix`: A matrix representing connections between nodes. - `distributions`: The input `distributions` list.
Examples
nodes <- data.frame(id = c("A", "B", "C", "D"))
links <- data.frame(source = c("A", "B", "C"), target = c("B", "C", "D"))
distributions <- list(
A = list(type = "discrete", values = c(0, 1), probs = c(0.5, 0.5)),
B = list(type = "normal", mean = 0, sd = 1),
C = list(type = "lognormal", meanlog = 0, sdlog = 0.5),
D = list(type = "uniform", min = 1, max = 5),
E = 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))
)
graph <- prob_net(nodes, links, distributions = distributions)