R: Accelerated Failure Time (AFT) Survival Regression Model

spark.survreg {SparkR}

R Documentation

Accelerated Failure Time (AFT) Survival Regression Model

Description

spark.survreg fits an accelerated failure time (AFT) survival regression model on a SparkDataFrame. Users can call summary to get a summary of the fitted AFT model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models.

Usage

spark.survreg(data, formula, ...)

## S4 method for signature 'AFTSurvivalRegressionModel,character'
write.ml(object, path,
  overwrite = FALSE)

## S4 method for signature 'SparkDataFrame,formula'
spark.survreg(data, formula, ...)

## S4 method for signature 'AFTSurvivalRegressionModel'
summary(object, ...)

## S4 method for signature 'AFTSurvivalRegressionModel'
predict(object, newData)

Arguments

`data`	A SparkDataFrame for training
`formula`	A symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', ':', '+', and '-'. Note that operator '.' is not supported currently
`object`	A fitted AFT survival regression model
`path`	The directory where the model is saved
`overwrite`	Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.
`newData`	A SparkDataFrame for testing

Value

spark.survreg returns a fitted AFT survival regression model

summary returns a list containing the model's coefficients, intercept and log(scale)

predict returns a SparkDataFrame containing predicted values on the original scale of the data (mean predicted value at scale = 1.0)

Note

write.ml(AFTSurvivalRegressionModel, character) since 2.0.0

spark.survreg since 2.0.0

summary(AFTSurvivalRegressionModel) since 2.0.0

predict(AFTSurvivalRegressionModel) since 2.0.0

Examples

## Not run: 
##D df <- createDataFrame(ovarian)
##D model <- spark.survreg(df, Surv(futime, fustat) ~ ecog_ps + rx)
##D 
##D # get a summary of the model
##D summary(model)
##D 
##D # make predictions
##D predicted <- predict(model, df)
##D showDF(predicted)
##D 
##D # save and load the model
##D path <- "path/to/model"
##D write.ml(model, path)
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)

[Package SparkR version 2.0.0 Index]