Generate Positive and Bounded Continuous Variables

Create generator specifications for gamma and beta variables.

Usage

gen_gamma(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  ...,
  scale_fixed_intercept = NULL,
  random_cov = NULL
)

gen_beta(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  ...,
  scale_fixed_intercept = NULL,
  random_cov = NULL
)

Arguments

vars

Character scalar naming the generated variable.

level

Simulation level. "single" generates row-level values, "level2" generates group-level values, and "multilevel" uses a random-intercept model.

fixed_intercept

Link-scale intercept. Gamma uses the log link and beta uses the logit link.

...

Removed direct distribution parameters are rejected.

scale_fixed_intercept

Log shape intercept for gamma variables or log precision intercept for beta variables.

random_cov

Group-level random-intercept covariance for multilevel generators.

Value

An mlsim_generator_spec object for use in simulate_data().

See also

Other predictor generators: count-generators, gen_categorical(), gen_custom(), gen_mvn(), gen_outcome(), gen_template()

Examples

sim <- simulate_data(
  n = 5,
  seed = 5,
  generators = list(
    positive = gen_gamma(
      "positive",
      fixed_intercept = log(1.5),
      scale_fixed_intercept = log(2)
    ),
    proportion = gen_beta(
      "proportion",
      fixed_intercept = stats::qlogis(0.4),
      scale_fixed_intercept = log(10)
    )
  )
)
sim$data
#>    obs_id  positive proportion
#>     <int>     <num>      <num>
#> 1:      1 0.4851945  0.4264494
#> 2:      2 0.6624663  0.2618874
#> 3:      3 1.1903527  0.6327267
#> 4:      4 3.2462519  0.5897335
#> 5:      5 0.6393495  0.5073695