Version:	0.2.4
Title:	Simulate and Sample from Ecological Interaction Networks
Description:	Randomly generate a wide range of interaction networks with specified size, average degree, modularity, and topological structure. Sample nodes and links from within simulated networks randomly, by degree, by module, or by abundance. Simulations and sampling routines are implemented in 'FORTRAN', providing efficient generation times even for large networks. Basic visualization methods also included. Algorithms implemented here are described in de Aguiar et al. (2017) <doi:10.48550/arXiv.1708.01242>.
License:	GPL-3
URL:	https://github.com/cboettig/EcoNetGen
BugReports:	https://github.com/cboettig/EcoNetGen/issues
Encoding:	UTF-8
ByteCompile:	true
RoxygenNote:	6.1.1
Suggests:	spelling, testthat, covr, ggraph
Imports:	igraph, ggplot2
Language:	en-US
NeedsCompilation:	yes
Packaged:	2023-08-27 04:46:36 UTC; cboettig
Author:	Marcus de Aguiar [aut, cph], Erica Newman [aut], Mathias Pires [aut], NIMBioS [fnd], Carl Boettiger [aut, cre]
Maintainer:	Carl Boettiger <cboettig@gmail.com>
Repository:	CRAN
Date/Publication:	2023-08-27 05:10:02 UTC

Plot network adjacency matrix

Description

Plot network adjacency matrix

Usage

adj_plot(graph)

Arguments

Examples

set.seed(12345)
graph <- netgen()
adj_plot(graph)

netgen

Description

Randomly generate a wide range of interaction networks

Usage

netgen(net_size = 50, ave_module_size = 10, min_module_size = 6,
  min_submod_size = 1, net_type = c("mixed", "random", "scalefree",
  "nested", "bi-partite nested", "bi-partite random",
  "tri-trophic bipartite nested-random",
  "tri-trophic bipartite nested-bipartite nested", "bn", "br", "tt-bn-r",
  "tt-bn-bn"), ave_degree = 5, rewire_prob_global = 0.2,
  rewire_prob_local = 0, mixing_probs = c(0.2, 0.2, 0.2, 0.2, 0.2, 0,
  0), verbose = FALSE)

Arguments

Details

network type is one of

• mixed

• random

• scalefree

• nested

• bi-partite nested (or short-hand "bn")

• bi-partite random (or short-hand "br")

• tri-trophic bipartite nested-random. (Can use short-hand "ttbnr")

• tri-trophic bipartite nested-bipartite nested (Can use short-hand "ttbnbn")

Valid Parameter Ranges

Please note that not all combinations of parameters will create valid networks. If an invalid combination is requested, netgen() will error with an informative message. A list of these constraints is provided below for reference.

1. net_size >= ave_module_size. If 'net_size = ave_module_size“ the program generates a network with a single module.

2. ave_module_size > min_module_size

3. ave_degree >= 1. Preferably larger than 4, to ensure single component modules.

4. rewire_prob_global = 0 produces completely uncoupled modules. To ensure a single component network use rewire_prob_global > 0 and sufficiently large.

5. rewire_prob_local = 0 produces idealized modules. Use rewire_prob_local > 0 to add stochasticity to the modules.

6. For tripartite networks min_module_size > min_submod_size. This also implies min_module_size >= 2.

7. For scalefree networks (or mixed networks involving scalefree modules) ave_degree < min_module_size

8. For mixed networks mixing_probs need to sum to 1. If the sum is larger than one, only the first types, corresponding to sum <=1, will be sampled.

Value

an igraph object

Examples

library(EcoNetGen)

set.seed(12345)
net <- netgen()
adj_plot(net)

netgen_v1

Description

netgen function

Usage

netgen_v1(n_modav = c(50, 10), cutoffs = c(3, 0), net_type = 1,
  net_degree = 10, net_rewire = c(0.3, 0), mod_probs = c(0.2, 0.2,
  0.2, 0.2, 0.2, 0, 0), verbose = FALSE)

Arguments

Details

network type

• 0 = mixed

• 1 = random

• 2 = scalefree

• 3 = nested

• 41 = bi-partite nested

• 42 = bi-partite random

• 51 = tri-trophic bipartite nested-random "ttbnr"

• 52 = tri-trophic bipartite nested-bipartite nested "ttbnbn"

Value

an igraph object

Network Sampling Routine

Description

Network Sampling Routine

Usage

netsampler(network_in, key_nodes_sampler = c("random", "lognormal",
  "Fisher log series", "exponential", "degree", "module"),
  neighbors_sampler = c("random", "exponential"), n_key_nodes = 10,
  n_neighbors = 0.5, hidden_modules = NULL, module_sizes = NULL,
  cluster_fn = igraph::cluster_edge_betweenness)

Arguments

Details

Algorithm first samples n_key_nodes according the the requested key_nodes_sampler criterion. For each key node, the requested number or fraction of neighbors is then sampled according to the neighbors_sampler criterion. Optionally, a list of modules can be designated as "hidden" and will be excluded from sampling.

if n_neighbors is greater than 1, assumes this is the number to sample. If n_neighborsis between 0 and 1, assumes this is the fration of neighbors to sample. (To sample 1 neighbor, use an explicit integer,1L (or as.integer(1)') to sample 100

Provide module_sizes list to improve performance. If not provided, this will will be calculated based on igraph::cluster_edge_betweeness. Be sure to provide a module_sizes vector whenever calling netsampler repeatedly on the same network to avoid unnecessary performance hit from recalculating modules every time. See examples.

Value

the original input network (as an igraph network object), with the attribute label added to the edges and vertices indicating if that edge or vertex was sampled or unsampled.

Examples

set.seed(12345)
net <- netgen()
sample <- netsampler(net)

## Precompute `module_sizes` for replicate sampling of the same network:
 library(igraph)
 modules <- cluster_edge_betweenness(as.undirected(net))
 module_sizes <- vapply(igraph::groups(modules), length, integer(1))
 sample <- netsampler(net, module_sizes = module_sizes)