Fit Models to Multiple Datasets

Fit TNA models to multiple datasets at once, with optional parallel processing.

Usage

batch_fit_models(
  data_list,
  model_type = c("tna", "ftna", "ctna", "atna"),
  parallel = FALSE,
  cores = NULL,
  progress = TRUE,
  ...
)

Arguments

data_list

A list of data frames, each containing sequence data.

model_type

Character. Type of model to fit: "tna", "ftna", "ctna", or "atna". Default: "tna".

parallel

Logical. Whether to use parallel processing. Default: FALSE.

cores

Integer or NULL. Number of cores to use for parallel processing. If NULL, uses parallel::detectCores() - 1. Ignored if parallel = FALSE. Default: NULL.

progress

Logical. Whether to show progress messages. Default: TRUE.

...

Additional arguments passed to the model fitting function.

Value

A list of fitted model objects, with the same names/indices as data_list. Failed fits are returned as NULL with a warning.

Details

This function provides a convenient way to fit TNA models to many datasets, such as when running simulations or analyzing multiple groups.

For parallel processing on Windows, set up a parallel backend first using the future package.

See also

fit_network_model for fitting a single model, batch_apply for applying functions to model lists.

Examples

if (FALSE) { # \dontrun{
# Generate multiple datasets
datasets <- lapply(1:10, function(i) {
  simulate_sequences(trans_mat, init_probs, max_seq_length = 20, num_rows = 100)
})

# Fit models in sequence
models <- batch_fit_models(datasets, model_type = "tna")

# Fit models in parallel
models <- batch_fit_models(datasets, model_type = "tna",
                           parallel = TRUE, cores = 4)

# Check results
sapply(models, function(m) if (!is.null(m)) "OK" else "Failed")
} # }