Assess split-half TNA reliability

Splits the sequences at random into two halves, fits the same estimator to each, and compares the two networks. Repeating that over iterations independent splits shows how stable an estimate this much data supports.

Usage

assess_tna_reliability(
  data,
  model = c("tna", "ftna", "ctna", "atna"),
  iterations = 100L,
  split = 0.5,
  format = c("auto", "long", "wide"),
  id = "id",
  period = "period",
  state = "state",
  seed = NULL,
  ...
)

Arguments

data

Sequence data accepted by fit_tna(), with at least 4 rows.

model

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

iterations

Number of random split halves. A single whole number of at least 2, defaulting to 100.

split

Fraction of the sequences assigned to the first half, rounded down. A single number strictly between 0 and 1, defaulting to 0.5. Each half must end up with at least two sequences.

format, id, period, state

Input-format arguments passed on when the sequences are prepared: format is one of "auto" (the default), "long" or "wide", and the other three name the identifier, period and state columns of long input.

seed

Optional seed. A single number, or NULL (the default).

...

Arguments passed to fit_tna().

Value

A plain base data.frame, not a simulab_sim, with one row per split, holding iteration, model and the compare_networks() agreement metrics for the two halves.

Examples

data <- simulate_sequences(n = 40, n_states = 3, chain_length = 12, seed = 1)
if (requireNamespace("tna", quietly = TRUE)) {
  assess_tna_reliability(data, model = "tna", iterations = 2, seed = 1)
}
#>   iteration model   pearson    cosine        mae       rmse jaccard edges_x
#> 1         1   tna 0.9607100 0.9786973 0.07297625 0.09823926       1       9
#> 2         2   tna 0.9930359 0.9966980 0.02929277 0.03707300       1       9
#>   edges_y
#> 1       9
#> 2       9