Spark ML -- Principal Components Analysis

Perform principal components analysis on a Spark DataFrame.

ml_pca(x, features = tbl_vars(x), k = length(features),
  ml.options = ml_options(), ...)

Arguments

x	An object coercable to a Spark DataFrame (typically, a `tbl_spark`).
features	The columns to use in the principal components analysis. Defaults to all columns in `x`.
k	The number of principal components.
ml.options	Optional arguments, used to affect the model generated. See `ml_options` for more details.
...	Optional arguments. The `data` argument can be used to specify the data to be used when `x` is a formula; this allows calls of the form `ml_linear_regression(y ~ x, data = tbl)`, and is especially useful in conjunction with `do`.