Simulate one transition network — simulate

Takes or draws a transition matrix, generates sequences from it with simulate_sequences(), and fits one transition network to the sequences with the tna package, all in one call.

Usage

simulate_tna(
  n = 100L,
  transition = NULL,
  chain_length = 20L,
  initial = NULL,
  states = NULL,
  n_states = 5L,
  state_categories = c("metacognitive", "cognitive"),
  model = c("tna", "ftna", "ctna", "atna"),
  seed = NULL,
  ...,
  batch = NULL
)

Arguments

n: Number of sequences, defaulting to 100.
transition: Generating transition matrix, or a tidy data frame with columns from, to and probability. NULL (the default) draws one over n_states states from a Dirichlet distribution.
chain_length: Sequence length, defaulting to 20.
initial: Initial probabilities or a fixed starting state. When NULL and transition is supplied, every sequence starts in the first state.
states: State labels.
n_states: Number of automatically generated states.
state_categories: Learning-state categories the labels of a drawn matrix come from, defaulting to c("metacognitive", "cognitive"); see learning_state_categories(). NULL labels the states State 1, State 2, and so on. Ignored when transition or states names the states.
model: TNA estimator, one of "tna" (the default), "ftna", "ctna" or "atna".
seed: Optional random seed.
...: Further arguments passed to simulate_sequences(), such as concentration or missing_tail.
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

The fitted network as a native tna model, of class c("simulab_tna", "tna"), so every tna function (plot(), centralities(), summary() and the rest) applies to it. Printing it shows the native tna print, then the generating transition matrix and initial probabilities. Tidy tables come from as.data.frame(): the fitted edges (the default; columns from, to and weight), the generating transitions (columns from, to and probability), initial_probabilities, the generated sequences in long form, wide and model_info. Requires the suggested tna package, and raises simulab_missing_tna without it.

Examples

if (requireNamespace("tna", quietly = TRUE)) {
  # With no arguments: a drawn 5-state system, 100 sequences, one network.
  network <- simulate_tna(seed = 1)
  network
  validate_recovery(network, network, term = "to", estimate = "weight")

  # From a given transition matrix.
  transition <- data.frame(
    from = rep(c("plan", "act"), each = 2),
    to = rep(c("plan", "act"), times = 2),
    probability = c(0.3, 0.7, 0.6, 0.4)
  )
  simulate_tna(n = 200, transition = transition, seed = 1)
}
#> State Labels : 
#> 
#>    act, plan 
#> 
#> Transition Probability Matrix :
#> 
#>            act      plan
#> act  0.3924374 0.6075626
#> plan 0.6983180 0.3016820
#> 
#> Initial Probabilities : 
#> 
#>  act plan 
#>    0    1 
#> 
#> Generating Transition Probability Matrix (truth) :
#> 
#>      act plan
#> act  0.4  0.6
#> plan 0.7  0.3
#> 
#> Generating Initial Probabilities (truth) :
#> 
#>  act plan 
#>    0    1 
#> 
#> Other tables: edges, sequences, wide, model_info. Read one with as.data.frame(x, what = "edges").