prepare_for_tna: Prepare Data for TNA Analysis

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Prepare Data for TNA Analysis

Description

Prepare simulated or real data for use with tna::tna() and related functions. Handles various input formats and ensures the output is compatible with TNA models.

Usage

prepare_for_tna(
  data,
  type = c("sequences", "long", "auto"),
  state_names = NULL,
  id_col = "Actor",
  time_col = "Time",
  action_col = "Action",
  validate = TRUE
)

Arguments

data	Data frame containing sequence data.
type	Character. Type of input data: "sequences" Wide format with one row per sequence (default). "long" Long format with one row per action. "auto" Automatically detect format based on column names.
state_names	Character vector. Expected state names, or NULL to extract from data. Default: NULL.
id_col	Character. Name of ID column for long format data. Default: "Actor".
time_col	Character. Name of time column for long format data. Default: "Time".
action_col	Character. Name of action column for long format data. Default: "Action".
validate	Logical. Whether to validate that all actions are in state_names. Default: TRUE.

Details

This function performs several preparations:

1. Converts long format to wide format if needed.

2. Validates that all actions/states are recognized.

3. Removes any non-sequence columns (e.g., id, metadata).

4. Converts factors to characters.

5. Ensures consistent column naming (V1, V2, ...).

Value

A data frame ready for use with TNA functions. For "sequences" type, returns a data frame where each row is a sequence and columns are time points (V1, V2, ...). For "long" type, converts to wide format first.

See Also

wide_to_long, long_to_wide for format conversions.

Examples

# From wide format sequences
sequences <- data.frame(
  V1 = c("A","B","C","A"), V2 = c("B","C","A","B"),
  V3 = c("C","A","B","C"), V4 = c("A","B","A","B")
)
tna_data <- prepare_for_tna(sequences, type = "sequences")

Nestimate documentation built on April 20, 2026, 5:06 p.m.