simulate.tna: Simulate Data from a Transition Network Analysis Model

In tna: Transition Network Analysis (TNA)

Simulate Data from a Transition Network Analysis Model

Description

Simulate Data from a Transition Network Analysis Model

Usage

## S3 method for class 'tna'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  max_len = 100L,
  na_range = c(0L, 0L),
  ...
)

Arguments

object	A tna object. The edge weights must be transition probabilities, i.e., the model must have type = "relative".
nsim	An integer giving the number of sequences to simulate. The default is 1.
seed	Ignored. Please use set.seed() manually.
max_len	An integer giving the maximum length of the simulated sequences. When no missing values are generated, this is the length of all simulated sequences.
na_range	An integer vector of length 2 giving the minimum and maximum number of missing values to generate for each sequence. The number of missing values is drawn uniformly from this range. If both values are zero (the default), no missing values are generated.
...	Ignored.

Value

A data.frame of the simulated sequence data with nsim rows and max_len columns.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), import_data(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), prepare_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), print.tna_data(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- tna(group_regulation)
sim <- simulate(model, nsim = 10, max_len = 10)

tna documentation built on June 8, 2025, 10:33 a.m.