Extract Pathways from Higher-Order Network Objects

Extracts higher-order pathway strings suitable for cograph::plot_simplicial(). Each pathway represents a multi-step dependency: source states lead to a target state.

For net_hon: extracts edges where the source node is higher-order (order > 1), i.e., the transitions that differ from first-order Markov.

For net_hypa: extracts anomalous paths (over- or under-represented relative to the hypergeometric null model).

For net_mogen: extracts all transitions at the optimal order (or a specified order).

Usage

pathways(x, ...)

# S3 method for class 'net_hon'
pathways(x, min_count = 1L, min_prob = 0, top = NULL, order = NULL, ...)

# S3 method for class 'net_hypa'
pathways(x, type = "all", ...)

# S3 method for class 'net_mogen'
pathways(x, order = NULL, min_count = 1L, min_prob = 0, top = NULL, ...)

Arguments

x: A higher-order network object (net_hon, net_hypa, or net_mogen).
...: Additional arguments.
min_count: Integer. Minimum transition count to include (default: 1).
min_prob: Numeric. Minimum transition probability to include (default: 0).
top: Integer or NULL. Return only the top N pathways ranked by count (default: NULL = all).
order: Integer or NULL. Markov order to extract. Default: optimal order from model selection.
type: Character. Which anomalies to include: "all" (default), "over", or "under".

Value

A character vector of pathway strings in arrow notation (e.g. "A B -> C"), suitable for cograph::plot_simplicial().

A character vector of pathway strings.

Methods (by class)

pathways(net_hon): Extract higher-order pathways from HON
pathways(net_hypa): Extract anomalous pathways from HYPA
pathways(net_mogen): Extract transition pathways from MOGen

Examples

# \donttest{
seqs <- list(c("A","B","C","D"), c("A","B","C","A"))
hon <- build_hon(seqs, max_order = 3)
pw <- pathways(hon)
# }