Simulate and fit repeated TNA networks

Generates repetitions sequence datasets with simulate_sequence_batches() and fits one transition network to each with fit_tna(), so the estimated networks can be held against the transition systems that generated them.

Usage

simulate_tna_batches(
  repetitions = 1L,
  model = c("tna", "ftna", "ctna", "atna"),
  ...,
  seed = NULL
)

Arguments

repetitions

Number of fitted networks. A single positive whole number, defaulting to 1, so a call with no arguments draws one transition system, generates sequences from it and fits one network.

model

TNA estimator, one of "tna" (the default), "ftna", "ctna" or "atna".

...

Arguments passed to simulate_sequences().

seed

Optional base seed. A single number, or NULL (the default). Dataset i uses seed + i - 1.

Value

A simulab_sim base data.frame of fitted edges, one row per dataset and from/to pair, with columns dataset, from, to and weight. Printing it shows true_transitions as the truth, keyed by the same dataset. Components: sequences, the generated sequences in long form with columns dataset, id, period and state; true_transitions, the generating probabilities with columns dataset, from, to and probability; and model_info, one row per dataset describing the fit.

Examples

if (requireNamespace("tna", quietly = TRUE)) {
  head(simulate_tna_batches(
    repetitions = 2, model = "tna", n = 20, n_states = 3, chain_length = 10, seed = 1
  ))

  # One network with no arguments, recovered against its own truth.
  network <- simulate_tna_batches(seed = 1)
  head(validate_recovery(network, network, term = "to", estimate = "weight"))
}
#>   dataset      from      term   estimate      truth         bias absolute_error
#> 1       1   Analyze   Analyze 0.56750572 0.55310055  0.014405175    0.014405175
#> 2       1 Elaborate   Analyze 0.05590062 0.07754096 -0.021640340    0.021640340
#> 3       1     Learn   Analyze 0.04237288 0.03569030  0.006682582    0.006682582
#> 4       1      Plan   Analyze 0.22473868 0.22135308  0.003385591    0.003385591
#> 5       1   Reflect   Analyze 0.13363029 0.11264179  0.020988504    0.020988504
#> 6       1   Analyze Elaborate 0.12356979 0.12152212  0.002047676    0.002047676
#>   relative_error recovered
#> 1     0.02604441      TRUE
#> 2    -0.27908269      TRUE
#> 3     0.18723806      TRUE
#> 4     0.01529498      TRUE
#> 5     0.18632964      TRUE
#> 6     0.01685024      TRUE