Spark ML  Generalized Linear Regression
Perform generalized linear regression on a Spark DataFrame.
ml_generalized_linear_regression(x, response, features, intercept = TRUE,
family = gaussian(link = "identity"), weights.column = NULL,
iter.max = 100L, ml.options = ml_options(), ...)
Arguments
x  An object coercable to a Spark DataFrame (typically, a

response  The name of the response vector (as a lengthone character
vector), or a formula, giving a symbolic description of the model to be
fitted. When 
features  The name of features (terms) to use for the model fit. 
intercept  Boolean; should the model be fit with an intercept term? 
family  The family / link function to use; analogous to those normally
passed in to calls to R's own 
weights.column  The name of the column to use as weights for the model fit. 
iter.max  The maximum number of iterations to use. 
ml.options  Optional arguments, used to affect the model generated. See

...  Optional arguments. The 
Details
In contrast to ml_linear_regression()
and
ml_logistic_regression()
, these routines do not allow you to
tweak the loss function (e.g. for elastic net regression); however, the model
fits returned by this routine are generally richer in regards to information
provided for assessing the quality of fit.
See also
Other Spark ML routines: ml_als_factorization
,
ml_decision_tree
,
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_pca
,
ml_random_forest
,
ml_survival_regression