Generate a transition system

Usage

generate_transition_system(
  n_states = 8L,
  states = NULL,
  concentration = 1,
  diagonal_concentration = 0,
  state_categories = c("metacognitive", "cognitive"),
  seed = NULL,
  batch = NULL
)

Arguments

n_states: Number of states.
states: Optional state labels.
concentration: Dirichlet concentration applied to every transition and to the initial-state probabilities.
diagonal_concentration: Optional extra concentration added to the self-transition (diagonal) entries only. The default 0 leaves self-transitions no more likely than any other transition.
state_categories: Learning-state categories the state labels are drawn from with sample_learning_states() when states is NULL, defaulting to c("metacognitive", "cognitive"); see learning_state_categories(). NULL labels the states State 1, State 2, and so on.
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 transition-edge simulab_sim base data.frame with n_states^2 rows and columns from, to, and probability; every from state's probabilities sum to one. An initial_probabilities table (state, probability) is available through as.data.frame().

Examples

result <- generate_transition_system(n_states = 3, seed = 1)
result
#> <simulab_sim:transition_system> 9 rows x 3 columns
#>      from      to probability
#> 1 Reflect Reflect  0.07830127
#> 2   Learn Reflect  0.55164885
#> 3    Plan Reflect  0.59019469
#> 4 Reflect   Learn  0.42202656
#> 5   Learn   Learn  0.35827360
#> 6    Plan   Learn  0.37885862
#> 7 Reflect    Plan  0.49967217
#> 8   Learn    Plan  0.09007756
#> 9    Plan    Plan  0.03094669
#> 
#> Other tables: initial_probabilities. Read one with as.data.frame(x, what = "initial_probabilities").