R: Isotonic Regression Model

spark.isoreg {SparkR}

R Documentation

Isotonic Regression Model

Description

Fits an Isotonic Regression model against a SparkDataFrame, similarly to R's isoreg(). Users can print, make predictions on the produced model and save the model to the input path.

Usage

spark.isoreg(data, formula, ...)

## S4 method for signature 'SparkDataFrame,formula'
spark.isoreg(data, formula,
  isotonic = TRUE, featureIndex = 0, weightCol = NULL)

## S4 method for signature 'IsotonicRegressionModel'
summary(object)

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

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

Arguments

`data`	SparkDataFrame for training.
`formula`	A symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.
`...`	additional arguments passed to the method.
`isotonic`	Whether the output sequence should be isotonic/increasing (TRUE) or antitonic/decreasing (FALSE).
`featureIndex`	The index of the feature if `featuresCol` is a vector column (default: 0), no effect otherwise.
`weightCol`	The weight column name.
`object`	a fitted IsotonicRegressionModel.
`newData`	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.

Value

spark.isoreg returns a fitted Isotonic Regression model.

summary returns summary information of the fitted model, which is a list. The list includes model's boundaries (boundaries in increasing order) and predictions (predictions associated with the boundaries at the same index).

predict returns a SparkDataFrame containing predicted values.

Note

spark.isoreg since 2.1.0

summary(IsotonicRegressionModel) since 2.1.0

predict(IsotonicRegressionModel) since 2.1.0

write.ml(IsotonicRegression, character) since 2.1.0

Examples

## Not run: 
##D sparkR.session()
##D data <- list(list(7.0, 0.0), list(5.0, 1.0), list(3.0, 2.0),
##D         list(5.0, 3.0), list(1.0, 4.0))
##D df <- createDataFrame(data, c("label", "feature"))
##D model <- spark.isoreg(df, label ~ feature, isotonic = FALSE)
##D # return model boundaries and prediction as lists
##D result <- summary(model, df)
##D # prediction based on fitted model
##D predict_data <- list(list(-2.0), list(-1.0), list(0.5),
##D                 list(0.75), list(1.0), list(2.0), list(9.0))
##D predict_df <- createDataFrame(predict_data, c("feature"))
##D # get prediction column
##D predict_result <- collect(select(predict(model, predict_df), "prediction"))
##D 
##D # save fitted model to input path
##D path <- "path/to/model"
##D write.ml(model, path)
##D 
##D # can also read back the saved model and print
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)

[Package SparkR version 3.0.0 Index]