Simulate a network matrix

Usage

simulate_network_matrix(
  nodes,
  type = c("adjacency", "transition", "frequency", "cooccurrence"),
  probability = 0.2,
  directed = TRUE,
  loops = FALSE,
  weighted = TRUE,
  weight_range = c(0.1, 1),
  frequency_mean = 10,
  seed = NULL,
  batch = NULL
)

Arguments

nodes: Number of nodes or unique node labels.
type: Matrix type.
probability: Probability of a non-zero dyad.
directed: Generate an asymmetric matrix where applicable.
loops: Permit non-zero diagonal entries.
weighted: Generate weighted adjacency/co-occurrence values. Ignored by the frequency and transition types, which are always weighted.
weight_range: Range for continuous weights.
frequency_mean: Mean positive count for frequency matrices; drawn as 1 + rpois(frequency_mean - 1), so entries are at least one.
seed: Optional seed.
batch: Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A tidy simulab_sim base data.frame holding the complete matrix in long form: nodes^2 rows with columns from, to, and value, zeros included. as.data.frame(x, what = ) also returns edges (the non-zero rows only), nodes (node), matrix (the wide form: a from column followed by one column per node), and a one-row settings table whose directed field reports whether the generated matrix is asymmetric. The transition type is row-stochastic: every from state's value entries sum to one.

Examples

result <- simulate_network_matrix(nodes = 8, type = "adjacency", seed = 1)
head(result)
#> <simulab_sim:network_matrix_adjacency> 6 rows x 3 columns
#>     from     to     value
#> 1 Node 1 Node 1 0.0000000
#> 2 Node 2 Node 1 0.0000000
#> 3 Node 3 Node 1 0.0000000
#> 4 Node 4 Node 1 0.0000000
#> 5 Node 5 Node 1 0.3846445
#> 6 Node 6 Node 1 0.0000000
#> 
#> Truth (settings):
#>        type directed loops weighted
#> 1 adjacency     TRUE FALSE     TRUE
#> 
#> Other tables: edges, nodes, matrix. Read one with as.data.frame(x, what = "edges").
components(result)
#>      table rows columns
#> 1     data   64       3
#> 2    edges    9       3
#> 3    nodes    8       1
#> 4   matrix    8       9
#> 5 settings    1       4