Spark ML  Survival Regression
Perform survival regression on a Spark DataFrame, using an Accelerated failure time (AFT) model with potentially rightcensored data.
ml_survival_regression(x, response, features, intercept = TRUE,
censor = "censor", 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? 
censor  The name of the vector that provides censoring information. This should be a numeric vector, with 0 marking uncensored data, and 1 marking rightcensored data. 
iter.max  The maximum number of iterations to use. 
ml.options  Optional arguments, used to affect the model generated. See

...  Optional arguments. The 
See also
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_pca
,
ml_random_forest