Simulate a temporal network as edge-activity spells

Usage

simulate_temporal_network(
  nodes,
  periods,
  initial_probability = 0.1,
  formation_probability = 0.05,
  dissolution_probability = 0.1,
  directed = TRUE,
  loops = FALSE,
  weight = c("binary", "uniform", "normal", "poisson"),
  weight_mean = 1,
  weight_sd = 1,
  weight_range = c(0.1, 1),
  node_type = NULL,
  seed = NULL,
  batch = NULL
)

Arguments

nodes: Number of nodes or unique node labels.
periods: Number of discrete observation periods.
initial_probability: Probability that a dyad is active initially.
formation_probability: Probability that an inactive dyad forms at the next period.
dissolution_probability: Probability that an active dyad dissolves at the next period.
directed: Generate directed dyads.
loops: Permit self-loops.
weight: Edge-spell weight distribution. One weight is drawn per spell and held constant while the spell is active.
weight_mean, weight_sd, weight_range: Weight parameters.
node_type: Optional node type for each node.
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 spell-level simulab_sim base data.frame with one row per activity spell and columns from, to, onset, terminus, weight, and censored. terminus is exclusive: the spell covers periods onset through terminus - 1, so a spell still active in the final period has terminus == periods + 1 and censored == TRUE. as.data.frame(x, what = ) also returns events (from, to, time, event – "formation" or "dissolution" – and weight, ordered by time), snapshots (one row per active dyad per period: period, from, to, weight), nodes (node, type), and a one-row settings table.

Examples

result <- simulate_temporal_network(
  nodes = 20, periods = 4, initial_probability = 0.1, seed = 1
)
head(result)
#> <simulab_sim:temporal_network> 6 rows x 6 columns
#>      from     to onset terminus weight censored
#> 1  Node 5 Node 1     1        5      1     TRUE
#> 2  Node 8 Node 1     1        5      1     TRUE
#> 3 Node 14 Node 1     2        5      1     TRUE
#> 4 Node 18 Node 1     2        3      1    FALSE
#> 5 Node 19 Node 1     1        5      1     TRUE
#> 6  Node 3 Node 2     1        3      1    FALSE
#> 
#> Truth (settings):
#>   periods directed loops initial_probability formation_probability
#> 1       4     TRUE FALSE                 0.1                  0.05
#>   dissolution_probability weight
#> 1                     0.1 binary
#> 
#> Other tables: events, snapshots, nodes. Read one with as.data.frame(x, what = "events").
components(result)
#>       table rows columns
#> 1      data   94       6
#> 2    events  117       5
#> 3 snapshots  225       4
#> 4     nodes   20       2
#> 5  settings    1       7