R: Spark ML - Survival Regression

ml_survival_regression {sparklyr} | R Documentation

## Spark ML – Survival Regression

### Description

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", 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. The `data` argument can be used to specify the data to be used when `x` is a formula; this allows calls of the form `ml_linear_regression(y ~ x, data = tbl)`, and is especially useful in conjunction with `do`.

### See Also

Other Spark ML routines: `ml_als_factorization`, `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`

[Package sparklyr version 0.5.4 Index]