Posterior distribution of network structure indices across ancestral networks

Calculate z-scores for nestedness (NODF) or modularity (Q) for each MCMC sample at time points in the past 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_samples(
  sampled_networks,
  index,
  ages = NULL,
  nnull = 100,
  use_future = FALSE
)

Arguments

sampled_networks: List of ancestral networks sampled across MCMC at given ages. Usually from the output of get_sampled_networks, see example.
index: Index to be calculated for each ancestral network. "NODF" to calculate nestedness or "Q" to calculate modularity.
ages: Vector of ages (time points in the past) of ancestral networks. By default, uses all ages present in sampled_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 samples and ages.

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"), burnin = 0)

# get sampled networks at ages in the past
ages <- c(60,50,40,0)
samples_at_ages <- posterior_at_ages(history, ages, tree, host_tree)
sampled_networks <- get_sampled_networks(samples_at_ages)

# calculate posterior distribution of nestedness
Nz <- index_at_ages_samples(sampled_networks, index = "NODF")

#  calculate posterior distribution of modularity with parallelization (slower)
# Qz <- index_at_ages_samples(sampled_networks, index = "Q", use_future = TRUE)