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", max.iter = 100L, ...)
- 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
responseis a formula, it is used in preference to other parameters to set the
interceptparameters (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
- 1in the model fit.
- 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; currently unused.
Other Spark ML routines: