spark.survreg {SparkR} | R Documentation |

`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.

spark.survreg(data, formula, ...) ## S4 method for signature 'SparkDataFrame,formula' spark.survreg(data, formula, aggregationDepth = 2) ## S4 method for signature 'AFTSurvivalRegressionModel' summary(object) ## S4 method for signature 'AFTSurvivalRegressionModel' predict(object, newData) ## S4 method for signature 'AFTSurvivalRegressionModel,character' write.ml(object, path, overwrite = FALSE)

`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. |

`...` |
additional arguments passed to the method. |

`aggregationDepth` |
The depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. This is an expert parameter. Default value should be good for most cases. |

`object` |
a fitted AFT survival regression model. |

`newData` |
a SparkDataFrame for testing. |

`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. |

`spark.survreg`

returns a fitted AFT survival regression model.

`summary`

returns summary information of the fitted model, which is a list.
The list includes the model's `coefficients`

(features, 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).

spark.survreg since 2.0.0

summary(AFTSurvivalRegressionModel) since 2.0.0

predict(AFTSurvivalRegressionModel) since 2.0.0

write.ml(AFTSurvivalRegressionModel, character) since 2.0.0

survival: https://cran.r-project.org/package=survival

```
## 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 2.2.3 Index]