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(), ...)
An object coercable to a Spark DataFrame (typically, a
The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
The name of features (terms) to use for the model fit.
Boolean; should the model be fit with an intercept term?
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.
The maximum number of iterations to use.
Optional arguments, used to affect the model generated. See
Optional arguments. The
Other Spark ML routines: