Simulate correlated ordinal variables

Usage

simulate_ordinal(
  n,
  probabilities,
  n_variables = 1L,
  rho = 0,
  structure = c("independent", "exchangeable", "ar1", "custom"),
  correlation = NULL,
  labels = NULL,
  variable_names = NULL,
  seed = NULL,
  batch = NULL
)

Arguments

n: Number of observations.
probabilities: A numeric vector used for every variable or a matrix with one row per variable and one column per category.
n_variables: Number of variables when probabilities is a vector.
rho, structure, correlation: Correlation arguments for the latent normal variables. structure follows the same resolution rule as simulate_correlated(): leaving it unset selects "exchangeable" when a non-zero rho is given. Passed to the latent Gaussian generator.
labels: Optional category labels.
variable_names: Optional variable names.
seed: Optional random seed.
batch: Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A simulab_sim base data.frame with one row per observation and columns id and one column per variable, each holding a category label. Two tidy tables come from as.data.frame(): what = "probabilities" has one row per variable and category with columns variable, category and probability; what = "correlation" has one row per latent correlation-matrix cell with columns row, column and correlation. That correlation is the latent Gaussian one, not the correlation of the realized categories, which is attenuated by the thresholding.

Examples

result <- simulate_ordinal(
  n = 200, probabilities = c(0.2, 0.5, 0.3), n_variables = 2, rho = 0.4, seed = 1
)
head(result)
#> <simulab_sim:ordinal> 6 rows x 3 columns
#>   id V1 V2
#> 1  1  2  2
#> 2  2  3  3
#> 3  3  2  3
#> 4  4  3  2
#> 5  5  2  1
#> 6  6  2  3
#> 
#> Truth (probabilities):
#>   variable category probability
#> 1       V1        1         0.2
#> 2       V1        2         0.5
#> 3       V1        3         0.3
#> 4       V2        1         0.2
#> 5       V2        2         0.5
#> 6       V2        3         0.3
#> 
#> Other tables: correlation. Read one with as.data.frame(x, what = "correlation").
as.data.frame(result, what = "probabilities")
#>   variable category probability
#> 1       V1        1         0.2
#> 2       V1        2         0.5
#> 3       V1        3         0.3
#> 4       V2        1         0.2
#> 5       V2        2         0.5
#> 6       V2        3         0.3