Perform principal components analysis on a Spark DataFrame.
ml_pca(x, features = tbl_vars(x), k = length(features),
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
.
The number of principal components.
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