Simulate Data from a Group Transition Network Analysis Model

Simulate Data from a Group Transition Network Analysis Model

Usage

# S3 method for class 'group_tna'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  max_len = 100L,
  na_range = c(0L, 0L),
  zero_row = "self",
  format = "wide",
  ...
)

Arguments

object

A group_tna object. The edge weights must be transition probabilities or frequencies, i.e., the model must have type = "relative" or type = "frequency".

nsim

An integer vector giving the number of sequences to simulate per group. If a single integer is provided, the same number of sequences is generated per each group. The default is 1.

seed

an object specifying if and how the random number generator should be initialized (‘seeded’).
For the "lm" method, either NULL or an integer that will be used in a call to set.seed before simulating the response vectors. If set, the value is saved as the "seed" attribute of the returned value. The default, NULL will not change the random generator state, and return .Random.seed as the "seed" attribute, see ‘Value’.

max_len

An integer vector giving the maximum length of the simulated sequences per group. When no missing values are generated, this is the length of all simulated sequences. If a single integer is provided, the maximum length is the same for each group.

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.

zero_row

A character string describing how to process zero rows in the weight matrix. The option "self" (the default) assigns probability 1 to the corresponding state (self loop) and option "uniform" assigns a uniform distribution.

format

A character string indicating whether the data should be returned in wide or long format.

...

Ignored.

Value

A data.frame of the simulated sequence data.

See also

Other data: import_data(), import_onehot(), prepare_data(), print.tna_data(), simulate.tna()

Examples

model <- group_tna(
  group_regulation,
  group = rep(c("High", "Low"), each = 1000)
)
sim <- simulate(model, nsim = 10, max_len = 10)