Calculate the AUROC for a diffusion score — calculate_diffusion

This functions can take a given NetworkDiffusions object and calculates an AUC and generates a Z-score based on random permutation of random_aucs for test for statistical significance if desired.

Usage

calculate_diffusion_auc(
  object,
  hit_nodes,
  auc_iters = 10000L,
  random_aucs = 1000L,
  permutation_test = FALSE,
  seed = 42L
)

Arguments

object: NetworkDiffusions object. The underlying class NetworkDiffusions().
hit_nodes: String vector. Which nodes in the graph are considered a 'hit'.
auc_iters: Integer. How many iterations to run to approximate the AUROC.
random_aucs: Integer. How many random AUROCs to calculate to estimate the Z-score. Only of relevance if permutation test is set to TRUE.
permutation_test: Boolean. Shall a permutation based Z-score be calculated.
seed: Integer. Random seed.

Value

List with AUC and Z-score as the two named elements if permutations test set to TRUE; otherwise just the AUC.

Examples

# AUROC of the diffusion score against two known hit nodes
set.seed(42)
g <- igraph::sample_pa(15, directed = FALSE)
edges <- data.table::setDT(igraph::as_data_frame(g))[, `:=`(
  from = sprintf("node_%i", from),
  to = sprintf("node_%i", to)
)]
object <- NetworkDiffusions(edges, weighted = FALSE, directed = FALSE)
object <- diffuse_seed_nodes(object, c(node_1 = 1, node_3 = 1), "max")
calculate_diffusion_auc(object, hit_nodes = c("node_2", "node_4"))
#> [1] 0.9239