ml_survival_regression
From sparklyr v0.2.31
by Javier Luraschi
Spark ML -- Survival Regression
Perform survival regression on a Spark DataFrame, using an Accelerated failure time (AFT) model with potentially right-censored data.
Usage
ml_survival_regression(x, response, features, intercept = TRUE, censor = "censor", max.iter = 100L, ...)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
responseis a formula, it is used in preference to other parameters to set theresponse,features, andinterceptparameters (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. - 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.
- max.iter
- The maximum number of iterations to use.
- ...
- Optional arguments; currently unused.
See Also
Other Spark ML routines: ml_decision_tree,
ml_generalized_linear_regression,
ml_gradient_boosted_trees,
ml_kmeans, ml_lda,
ml_linear_regression,
ml_logistic_regression,
ml_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest, ml_pca,
ml_random_forest
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