Simulate grouped actor sequences

Usage

simulate_group_sequences(
  groups = 2L,
  actors = 50L,
  transitions = NULL,
  chain_length = 20L,
  initial = NULL,
  group_names = NULL,
  states = NULL,
  n_states = 5L,
  state_categories = c("metacognitive", "cognitive"),
  seed = NULL,
  ...,
  batch = NULL
)

Arguments

groups: Number of groups, defaulting to 2.
actors: Actors per group, scalar or one value per group, defaulting to 50.
transitions: A common transition matrix, one matrix per group, or NULL for randomly generated matrices. A tidy data frame with columns group, from, to and probability gives one matrix per group.
chain_length: Sequence length, defaulting to 20.
initial: Common initial probabilities, one vector per group, or NULL. A tidy data frame with columns group, state and probability gives one vector per group.
group_names: Optional group labels.
states, n_states, state_categories: State-space arguments passed to simulate_sequences().
seed: Optional random seed. Group-specific deterministic offsets are used without leaking RNG state.
...: Advanced sequence arguments passed to simulate_sequences().
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 long-form simulab_sim base data.frame with one row per actor sequence position and columns group, id, period, and state. Actor identifiers are made unique across groups as G<group>_A<actor>. as.data.frame(x, what = ) also returns transitions (group, from, to, probability), wide (one row per actor: id, S1, S2, ..., group), and groups (group, actors).

Examples

result <- simulate_group_sequences(
  groups = 2, actors = 20, chain_length = 12, n_states = 3, seed = 1
)
head(result)
#> <simulab_sim:group_sequences> 6 rows x 4 columns
#>     group    id period   state
#> 1 Group 1 G1_A1      1    Plan
#> 2 Group 1 G1_A1      2   Learn
#> 3 Group 1 G1_A1      3   Learn
#> 4 Group 1 G1_A1      4   Learn
#> 5 Group 1 G1_A1      5 Reflect
#> 6 Group 1 G1_A1      6   Learn
#> 
#> Truth (transitions):
#>      group    from      to probability
#> 1  Group 1 Reflect Reflect  0.07830127
#> 2  Group 1   Learn Reflect  0.55164885
#> 3  Group 1    Plan Reflect  0.59019469
#> 4  Group 1 Reflect   Learn  0.42202656
#> 5  Group 1   Learn   Learn  0.35827360
#> 6  Group 1    Plan   Learn  0.37885862
#> 7  Group 1 Reflect    Plan  0.49967217
#> 8  Group 1   Learn    Plan  0.09007756
#> 9  Group 1    Plan    Plan  0.03094669
#> 10 Group 2 Reflect Reflect  0.08698614
#> ... 8 more rows
#> 
#> Other tables: wide, groups. Read one with as.data.frame(x, what = "wide").
components(result)
#>         table rows columns
#> 1        data  480       4
#> 2 transitions   18       4
#> 3        wide   40      14
#> 4      groups    2       2