Generate random samples from a t-distribution




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


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


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()