Generate Positive and Bounded Continuous Variables

Create generator specifications for gamma and beta variables.

Usage

gen_gamma(
  vars,
  level = c("single", "level2", "multilevel"),
  shape = NULL,
  mean = NULL,
  rate = NULL,
  scale = NULL,
  fixed_intercept = NULL,
  random_cov = NULL,
  scale_fixed_intercept = NULL
)

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

shape

Gamma shape parameter.

mean

Mean parameter. For gamma variables this must be positive; for beta variables it must be in (0, 1).

rate, scale

Alternative gamma parameterizations for "single" and "level2" generators. Supply at most one of rate or scale.

fixed_intercept

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

random_cov

Group-level random-intercept covariance for multilevel generators.

scale_fixed_intercept

Optional log shape or log precision intercept for multilevel generators.

precision

Beta precision parameter.

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_normal()

Examples

sim <- simulate_data(
  n = 5,
  seed = 5,
  generators = list(
    positive = gen_gamma("positive", shape = 2, mean = 1.5),
    proportion = gen_beta("proportion", mean = 0.4, precision = 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