Fit a temporal network analysis model

Usage

fit_tna(
  data,
  model = c("tna", "ftna", "ctna", "atna"),
  format = c("auto", "long", "wide"),
  id = "id",
  period = "period",
  state = "state",
  group = NULL,
  ...
)

Arguments

data: Long- or wide-form sequence data.
model: TNA, frequency TNA, co-occurrence TNA, or attention TNA.
format: Input format. auto recognizes canonical long data.
id, period, state: Canonical long-form column names.
group: Optional grouping-column name. Supplying it fits a model per group through the corresponding grouped TNA estimator.
...: Additional arguments passed to the estimator in tna.

Value

A tidy edge-list simulab_sim base data.frame with one row per ordered state pair and columns from, to, and weight, prefixed by a group column when group is supplied. For model = "tna" the weights are the estimated transition probabilities and sum to one within each from state; the other estimators return their own (not row-stochastic) weights. as.data.frame(x, what = ) also returns initial_probabilities (state, probability) and model_info (model, grouped, group, observations, sequence_positions). Use as_tna_model() when a native model object is required by tna plotting or inference functions.

Examples

data <- simulate_sequences(n = 40, n_states = 3, chain_length = 12, seed = 1)
if (requireNamespace("tna", quietly = TRUE)) {
  head(fit_tna(data, model = "tna"))
}
#> <simulab_sim:tna_model> 6 rows x 3 columns
#>      from    to     weight
#> 1   Learn Learn 0.53112033
#> 2    Plan Learn 0.17187500
#> 3 Reflect Learn 0.90370370
#> 4   Learn  Plan 0.10788382
#> 5    Plan  Plan 0.17187500
#> 6 Reflect  Plan 0.03703704
#> 
#> Other tables: initial_probabilities, model_info. Read one with as.data.frame(x, what = "initial_probabilities").