Generate random samples from a hypergeometric distribution — sdf_rhyper • sparklyr

Generator method for creating a single-column Spark dataframes comprised of i.i.d. samples from a hypergeometric distribution.

Usage

sdf_rhyper(
  sc,
  nn,
  m,
  n,
  k,
  num_partitions = NULL,
  seed = NULL,
  output_col = "x"
)

Arguments

sc: A Spark connection.
nn: Sample Size.
m: The number of successes among the population.
n: The number of failures among the population.
k: The number of draws.
num_partitions: Number of partitions in the resulting Spark dataframe (default: default parallelism of the Spark cluster).
seed: Random seed (default: a random long integer).
output_col: Name of the output column containing sample values (default: "x").

See also

Other Spark statistical routines: sdf_rbeta(), sdf_rbinom(), sdf_rcauchy(), sdf_rchisq(), sdf_rexp(), sdf_rgamma(), sdf_rgeom(), sdf_rlnorm(), sdf_rnorm(), sdf_rpois(), sdf_rt(), sdf_runif(), sdf_rweibull()