Generate Categorical Variables

Create a generator specification for binary or multicategory categorical variables.

Usage

gen_categorical(
  vars,
  level = c("single", "level2", "multilevel"),
  categories = NULL,
  prob = NULL,
  fixed_intercept = NULL,
  random_cov = NULL,
  reference = NULL,
  output = c("factor", "character", "integer"),
  ordered = FALSE
)

Arguments

vars

Character scalar naming the generated variable.

level

Simulation level. "single" generates row-level categories, "level2" generates group-level categories, and "multilevel" uses a baseline-category logit random-intercept model.

categories

Vector of category values. Defaults to c(0L, 1L).

prob

Category probabilities for "single" and "level2" generators. For binary variables a scalar is interpreted as the success probability for the second category. Multicategory probabilities may be a vector or row-wise matrix.

fixed_intercept

Baseline-category logits. For k categories this has length k - 1 and is named or ordered by non-reference category.

random_cov

Multilevel covariance matrix for baseline-category random intercepts.

reference

Reference category for the baseline-category logit model. Defaults to the first category.

output

Output type: "factor", "character", or "integer". Integer output uses zero-based category codes.

ordered

Logical; when TRUE, return an ordered factor. Requires output = "factor".

Value

An mlsim_generator_spec object for use in simulate_data().

See also

Other predictor generators: continuous-generators, count-generators, gen_custom(), gen_mvn(), gen_normal()

Examples

sim <- simulate_data(
  n = 6,
  seed = 3,
  generators = list(
    arm = gen_categorical(
      "arm",
      categories = c("control", "treatment"),
      prob = 0.5
    )
  )
)
sim$data
#>    obs_id       arm
#>     <int>    <fctr>
#> 1:      1   control
#> 2:      2 treatment
#> 3:      3   control
#> 4:      4   control
#> 5:      5 treatment
#> 6:      6 treatment