Simulate from an empirical kernel density

Simulate from an empirical kernel density

Usage

simulate_density(
  n,
  values,
  variable = "value",
  use_limits = FALSE,
  keep_missing = FALSE,
  id = "id",
  seed = NULL,
  batch = NULL
)

Arguments

n

Number of observations.

values

Numeric source values, of which at least two must be present and distinct. Missing values are dropped before the density is estimated.

variable

Output variable name.

use_limits

Constrain draws to the observed range.

keep_missing

Draw missing values at the source's missing-data rate, so the proportion is reproduced in expectation rather than exactly.

id

Identifier variable name.

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 n rows and columns id and variable, one generated density value per row. as.data.frame(x, what = "source") gives a one-row summary of the source values.

Examples

values <- stats::rnorm(200, mean = 5, sd = 2)
result <- simulate_density(n = 100, values = values, variable = "score", seed = 1)
head(result)
#> <simulab_sim:density> 6 rows x 2 columns
#>   id    score
#> 1  1 6.719921
#> 2  2 3.118475
#> 3  3 3.888691
#> 4  4 4.753282
#> 5  5 4.587366
#> 6  6 4.722838
#> 
#> Other tables: source. Read one with as.data.frame(x, what = "source").