ml_survival_regression

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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 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.
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

Aliases
  • ml_survival_regression
Documentation reproduced from package sparklyr, version 0.2.28, License: file LICENSE

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