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Generate Count Variables

Source: R/sim-generators.R
count-generators.Rd

Create generator specifications for binomial, Poisson, and negative binomial count variables.

Usage

gen_binomial(
  vars,
  level = c("single", "level2", "multilevel"),
  size = 1,
  prob = NULL,
  fixed_intercept = NULL,
  random_cov = NULL
)

gen_poisson(
  vars,
  level = c("single", "level2", "multilevel"),
  lambda = NULL,
  fixed_intercept = NULL,
  random_cov = NULL
)

gen_negbin(
  vars,
  level = c("single", "level2", "multilevel"),
  size = NULL,
  mu = 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.

size

Binomial trial size or negative-binomial size/dispersion parameter. May be a scalar, vector, function of n, or count-distribution list where supported.

prob

Binomial success probability for "single" and "level2" generators.

fixed_intercept

Link-scale intercept for direct-parameter defaults and multilevel models. Binomial uses the logit link; Poisson and negative binomial use the log link.

random_cov

Group-level random-intercept covariance for multilevel generators.

lambda

Poisson mean for "single" and "level2" generators.

mu

Negative-binomial mean for "single" and "level2" generators.

scale_fixed_intercept

Optional log size intercept for multilevel negative-binomial generators.

Value

An mlsim_generator_spec object for use in simulate_data().

Details

For multilevel count generators, fixed_intercept and random_cov define the link-scale mean. Non-multilevel generators can use direct distribution parameters or a link-scale fixed_intercept.

See also

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

Examples

sim <- simulate_data(
  n = 5,
  seed = 4,
  generators = list(
    events = gen_poisson("events", lambda = 2),
    successes = gen_binomial("successes", size = 4, prob = 0.5),
    overdispersed = gen_negbin("overdispersed", size = 3, mu = 2)
  )
)
sim$data
#>    obs_id events successes overdispersed
#>     <int>  <int>     <int>         <num>
#> 1:      1      2         1             1
#> 2:      2      0         3             4
#> 3:      3      1         3             8
#> 4:      4      1         4             8
#> 5:      5      3         1             2