Fit TNA to a sequence sample

Usage

sample_tna(
  data,
  fraction = 0.3,
  replace = FALSE,
  seed = NULL,
  format = c("auto", "long", "wide"),
  id = "id",
  period = "period",
  state = "state",
  group = NULL,
  ...
)

Arguments

data: Sequence data, in long or wide form.
fraction: Single number in (0, 1] giving the fraction of sequences to draw, default 0.3. At least two sequences are always drawn.
replace: Single flag, whether to draw with replacement. Default FALSE.
seed: Optional single seed, restored on exit.
format: One of "auto", "long" or "wide", naming the layout of data. The default "auto" detects it.
id, period, state: Column names identifying the sequence, the position within it, and the state. Used only when data is in long form; the defaults are "id", "period" and "state".
group: Optional column name. When given, sequences are sampled within each group and the refit is grouped.
...: Further arguments passed to fit_tna(), such as model. The sampled sequences are always refitted in wide form, so format is consumed by the sampling step and is not forwarded.

Value

The fit_tna() result for the sampled sequences: a simulab_sim edge list with one row per transition and the columns from, to and weight, plus the initial_probabilities and model_info components.

Examples

data <- simulate_sequences(n = 40, n_states = 3, chain_length = 12, seed = 1)
if (requireNamespace("tna", quietly = TRUE)) {
  head(sample_tna(data, fraction = 0.5, seed = 1))
}
#> <simulab_sim:tna_model> 6 rows x 3 columns
#>      from    to     weight
#> 1   Learn Learn 0.51282051
#> 2    Plan Learn 0.15151515
#> 3 Reflect Learn 0.87142857
#> 4   Learn  Plan 0.11111111
#> 5    Plan  Plan 0.27272727
#> 6 Reflect  Plan 0.02857143
#> 
#> Other tables: initial_probabilities, model_info. Read one with as.data.frame(x, what = "initial_probabilities").