Cross-validate TNA estimators

Each iteration splits the sequences once into a training and a testing set, fits every estimator to both halves, and compares the two networks with compare_networks().

Usage

cross_validate_tna(
  data,
  models = c("tna", "ftna", "ctna", "atna"),
  iterations = 20L,
  training_fraction = 0.7,
  format = c("auto", "long", "wide"),
  id = "id",
  period = "period",
  state = "state",
  seed = NULL,
  ...
)

Arguments

data

Sequence data in long or wide form.

models

Character vector of TNA estimators to compare, any of "tna", "ftna", "ctna", "atna". The default runs all four.

iterations

Number of train/test splits, a whole number of at least 1, default 20.

training_fraction

Fraction of sequences assigned to the training half, strictly between 0 and 1, default 0.7. At least two training sequences are always used, and a split leaving fewer than two testing sequences is an error.

format, id, period, state

Arguments describing the shape and column names of data. They are used to reshape data before splitting; both halves are then fitted in wide form.

seed

Optional single seed, restored on exit.

...

Further arguments passed to fit_tna().

Value

A plain base data.frame with one row per iteration and estimator and the columns iteration, model, and the compare_networks() agreement metrics pearson, cosine, mae, rmse, jaccard, edges_x and edges_y. This is not a simulab_sim.

Examples

data <- simulate_sequences(n = 60, n_states = 3, chain_length = 12, seed = 1)
if (requireNamespace("tna", quietly = TRUE)) {
  cross_validate_tna(data, models = c("tna", "ftna"), iterations = 2, seed = 1)
}
#>   iteration model   pearson    cosine         mae        rmse   jaccard edges_x
#> 1         1   tna 0.9838410 0.9929185  0.04560538  0.05282897 0.8888889       9
#> 2         1  ftna 0.9832673 0.9917742 29.77777778 42.07664963 0.8888889       9
#> 3         2   tna 0.9903498 0.9954154  0.03047942  0.04354214 1.0000000       9
#> 4         2  ftna 0.9929774 0.9947696 29.33333333 43.53542619 1.0000000       9
#>   edges_y
#> 1       8
#> 2       8
#> 3       9
#> 4       9