Bootstrap a TNA model

Usage

bootstrap_tna(
  data,
  model = c("tna", "ftna", "ctna", "atna"),
  repetitions = 100L,
  fraction = 1,
  format = c("auto", "long", "wide"),
  id = "id",
  period = "period",
  state = "state",
  group = NULL,
  seed = NULL,
  ...
)

Arguments

data: Sequence data in long or wide form.
model: TNA estimator, one of "tna" (the default), "ftna", "ctna", "atna".
repetitions: Single whole number of bootstrap samples, at least 2, default 100.
fraction: Single number in (0, 1] giving the fraction of sequences drawn with replacement in each repetition, default 1. At least two sequences are always drawn.
format, id, period, state, group: Arguments describing the shape and column names of data. They are used to reshape data before resampling; the resampled sequences are then fitted in wide form, so only group is forwarded to fit_tna(). group resamples within each group.
seed: Optional single seed, restored on exit and recorded on the result.
...: Further arguments passed to fit_tna().

Value

A simulab_sim edge list with one row per repetition and transition: a leading iteration column followed by the columns fit_tna() returns. The summary component holds one row per edge (per group, when group is given) with the bootstrap mean, sd, and the 2.5% and 97.5% percentiles as lower and upper.

Examples

data <- simulate_sequences(n = 40, n_states = 3, chain_length = 12, seed = 1)
if (requireNamespace("tna", quietly = TRUE)) {
  head(bootstrap_tna(data, model = "tna", repetitions = 5, seed = 1))
}
#> <simulab_sim:tna_bootstrap> 6 rows x 4 columns
#>   iteration    from    to     weight
#> 1         1   Learn Learn 0.57661290
#> 2         1    Plan Learn 0.16666667
#> 3         1 Reflect Learn 0.88636364
#> 4         1   Learn  Plan 0.08870968
#> 5         1    Plan  Plan 0.21666667
#> 6         1 Reflect  Plan 0.02272727
#> 
#> Other tables: summary. Read one with as.data.frame(x, what = "summary").