caugi (version 1.0.0)

as_caugi: Convert to a caugi

Description

Convert an object to a caugi. The object can be a graphNEL, matrix, tidygraph, daggity, bn, or igraph.

Usage

as_caugi(
  x,
  class = c("DAG", "PDAG", "ADMG", "PAG", "UNKNOWN"),
  simple = TRUE,
  build = TRUE,
  collapse = FALSE,
  collapse_to = "---",
  ...
)

Value

A caugi object.

Arguments

x

An object to convert to a caugi.

class

"DAG", "PDAG", "ADMG", "PAG", or "UNKNOWN". "PAG" is only supported for integer coded matrices. "ADMG" is for Acyclic Directed Mixed Graphs (with --> and <-> edges).

simple

logical. If TRUE (default) the graph will be simple (no multiple edges or self-loops).

build

logical. If TRUE (default) build the graph now, otherwise build lazily on first query or when using build().

collapse

logical. If TRUE collapse mutual directed edges to undirected edges. Default is FALSE.

collapse_to

Character string to use as the edge glyph when collapsing. Should be a registered symmetrical edge glyph. Default is "---".

...

Additional arguments passed to specific methods.

Details

For matrices, as_caugi assumes that the rows are the from nodes and the columns are the to nodes. Thus, for a graph, G: A --> B, we would have that G["A", "B"] == 1 and G["B", "A"] == 0. For PAGs, the integer codes are as follows (as used in pcalg):

• 0: no edge

• 1: circle (e.g., A o-o B or A o-- B)

• 2: arrowhead (e.g., A --> B or A o-> B)

• 3: tail (e.g., A o-- B or A --- B)

See Also

Other conversions: as_adjacency(), as_bnlearn(), as_dagitty(), as_igraph()

Examples

# igraph
ig <- igraph::graph_from_literal(A - +B, B - +C)
cg_ig <- as_caugi(ig, class = "DAG")

# graphNEL
gn <- graph::graphNEL(nodes = c("A", "B", "C"), edgemode = "directed")
gn <- graph::addEdge("A", "B", gn)
gn <- graph::addEdge("B", "C", gn)
cg_gn <- as_caugi(gn, class = "DAG")

# adjacency matrix
m <- matrix(0L, 3, 3, dimnames = list(LETTERS[1:3], LETTERS[1:3]))
m["A", "B"] <- 1L
m["B", "C"] <- 1L
cg_adj <- as_caugi(m, class = "DAG")

# bnlearn
bn <- bnlearn::model2network("[A][B|A][C|B]")
cg_bn <- as_caugi(bn, class = "DAG")

# dagitty
dg <- dagitty::dagitty("dag {
 A -> B
 B -> C
 }")
cg_dg <- as_caugi(dg, class = "DAG")

cg <- caugi(A %-->% B %-->% C, class = "DAG")

# check that all nodes are equal in all graph objects
for (cg_converted in list(cg_ig, cg_gn, cg_adj, cg_bn, cg_dg)) {
  stopifnot(identical(nodes(cg), nodes(cg_converted)))
  stopifnot(identical(edges(cg), edges(cg_converted)))
}

# collapse mutual edges
ig2 <- igraph::graph_from_literal(A - +B, B - +A, C - +D)
cg2 <- as_caugi(ig2, class = "PDAG", collapse = TRUE, collapse_to = "---")

# coded integer matrix for PAGs (pcalg style)
nm <- c("A", "B", "C", "D")
M <- matrix(0L, 4, 4, dimnames = list(nm, nm))

# A --> B
M["A", "B"] <- 2L # mark at B end
M["B", "A"] <- 3L # mark at A end

# A --- C
M["A", "C"] <- 3L
M["C", "A"] <- 3L

# B o-> C
M["B", "C"] <- 2L
M["C", "B"] <- 1L

# C o-o D
M["C", "D"] <- 1L
M["D", "C"] <- 1L

cg <- as_caugi(M, class = "PAG")