Simulate a grouped TNA transition network

Usage

simulate_tna_network(
  groups,
  nodes_per_group,
  group_names = NULL,
  within_probability = 0.4,
  between_probability = 0.15,
  loops = FALSE,
  seed = NULL,
  batch = NULL
)

Arguments

groups: Number of node groups.
nodes_per_group: Nodes per group, scalar or vector.
group_names: Optional group names.
within_probability, between_probability: Edge probabilities within and between node groups.
loops: Permit self transitions when drawing the underlying edges. A node left with no outgoing edge is nevertheless given a self transition of probability one, so self-loops can appear even when loops is FALSE.
seed: Optional random 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 non-zero transitions only, one row per edge, with columns from, to, and probability. as.data.frame(x, what = ) also returns nodes (node, group), adjacency (the long row/column/weight table of the underlying directed Bernoulli graph), and transitions (the full nodes^2-row transition table including zeros). The transition matrix is row-stochastic: every from node's probabilities sum to one.

Examples

result <- simulate_tna_network(groups = 3, nodes_per_group = 4, seed = 1)
head(result)
#> <simulab_sim:tna_network> 6 rows x 3 columns
#>     from    to probability
#> 5  G2_N1 G1_N1   0.3333333
#> 7  G2_N3 G1_N1   0.3333333
#> 8  G2_N4 G1_N1   0.3333333
#> 15 G1_N3 G1_N2   0.3333333
#> 20 G2_N4 G1_N2   0.3333333
#> 23 G3_N3 G1_N2   0.3333333
#> 
#> Truth (transitions):
#>     from    to probability
#> 1  G1_N1 G1_N1   0.0000000
#> 2  G1_N2 G1_N1   0.0000000
#> 3  G1_N3 G1_N1   0.0000000
#> 4  G1_N4 G1_N1   0.0000000
#> 5  G2_N1 G1_N1   0.3333333
#> 6  G2_N2 G1_N1   0.0000000
#> 7  G2_N3 G1_N1   0.3333333
#> 8  G2_N4 G1_N1   0.3333333
#> 9  G3_N1 G1_N1   0.0000000
#> 10 G3_N2 G1_N1   0.0000000
#> ... 134 more rows
#> 
#> Other tables: nodes, adjacency. Read one with as.data.frame(x, what = "nodes").
components(result)
#>         table rows columns
#> 1        data   27       3
#> 2       nodes   12       2
#> 3   adjacency  144       3
#> 4 transitions  144       3