Generate random samples from a t-distribution

sdf_rt

Description

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

Usage

sdf_rt(sc, n, df, num_partitions = NULL, seed = NULL, output_col = "x")

Arguments

Arguments	Description
sc	A Spark connection.
n	Sample Size (default: 1000).
df	Degrees of freedom (> 0, maybe non-integer).
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_rhyper(), sdf_rlnorm(), sdf_rnorm(), sdf_rpois(), sdf_runif(), sdf_rweibull()