Spark ML -- Survival Regression

Usage

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; currently unused.

Description

Perform survival regression on a Spark DataFrame, using an Accelerated failure time (AFT) model with potentially right-censored data.