Spark ML -- Survival Regression

Perform survival regression on a Spark DataFrame, using an Accelerated failure time (AFT) model with potentially right-censored 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 `tbl_spark`).
response	The name of the response vector (as a length-one character vector), or a formula, giving a symbolic description of the model to be fitted. When `response` is a formula, it is used in preference to other parameters to set the `response`, `features`, and `intercept` parameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g. `response ~ feature1 + feature2 + ...`. The intercept term can be omitted by using `- 1` in the model fit.
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 right-censored data.
iter.max	The maximum number of iterations to use.
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`.