Network structure indices across ancestral summary networks and extant network

Calculate z-scores for nestedness (NODF) or modularity (Q) for each ancestral network at time points in the past and the extant network, based on null networks where all interactions have the same probability. By calculating z-scores, we can compare ancestral networks at different ages.

Usage

index_at_ages_summary(
  summary_networks,
  index,
  ages = NULL,
  nnull = 100,
  use_future = FALSE
)

Arguments

summary_networks: List of ancestral networks at given ages and extant network. Usually from the output of get_summary_networks, see example.
index: Index to be calculated for each ancestral network. "NODF" to calculate nestedness or "Q" to calculate modularity. For weighted networks, "weighted NODF" will be calculated.
ages: Vector of ages (time points in the past) of ancestral networks. By default, uses all ages present in summary_networks.
nnull: Number of null networks to generate to calculate the z-score. Default is 100.
use_future: Parallelize with package future? Logical. Only applicable to modularity calculation.

Value

A data.frame of z-scores and p-values across networks.

Examples

# read data that comes with the package
data_path <- system.file("extdata", package = "evolnets")
tree <- read_tree_from_revbayes(paste0(data_path,"/tree_pieridae.tre"))
host_tree <- ape::read.tree(paste0(data_path,"/host_tree_pieridae.phy"))
history <- read_history(paste0(data_path,"/history_thin_pieridae.txt"))

# get ancestral networks at ages in the past
ages <- c(60, 50, 40, 0)
at_ages <- posterior_at_ages(history, ages, tree, host_tree)
summary_networks <- get_summary_networks(at_ages, threshold = 0.5, weighted = TRUE)

# calculate nestedness of ancestral and extant networks
Nz <- index_at_ages_summary(summary_networks, index = "NODF")

# calculate modularity of ancestral and extant networks with parallelization (slower)
# Qz <- index_at_ages_summary(summary_networks, index = "Q", use_future = TRUE)