| 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,
stringIndexerOrderType = c("frequencyDesc", "frequencyAsc", "alphabetDesc",
"alphabetAsc")
)
## 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. |
stringIndexerOrderType |
how to order categories of a string feature column. This is used to decide the base level of a string feature as the last category after ordering is dropped when encoding strings. Supported options are "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc". The default value is "frequencyDesc". When the ordering is set to "alphabetDesc", this drops the same category as R when encoding strings. |
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)