Generate random samples from a hypergeometric distribution
R/sdf_stat.R
sdf_rhyper
Description
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
| Arguments | Description |
|---|---|
| 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()