Perform principal components analysis on a Spark DataFrame.
ml_pca(x, features = dplyr::tbl_vars(x), ml.options = ml_options(), ...)An object coercable to a Spark DataFrame (typically, a
tbl_spark).
The columns to use in the principal components
analysis. Defaults to all columns in x.
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.
Other Spark ML routines: ml_als_factorization,
ml_decision_tree,
ml_generalized_linear_regression,
ml_gradient_boosted_trees,
ml_kmeans, ml_lda,
ml_linear_regression,
ml_logistic_regression,
ml_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest,
ml_random_forest,
ml_survival_regression