Spark ML -- Survival Regression
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
response is a formula, it is used in preference to other
parameters to set the
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.
- 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
ml_options for more details.
- Optional arguments; currently unused.
Perform survival regression on a Spark DataFrame, using an Accelerated
failure time (AFT) model with potentially right-censored data.
Other Spark ML routines: