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'
predict(object, newData)

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

## 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.

predict returns a SparkDataFrame containing predicted values.

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).

Note

spark.isoreg since 2.1.0

predict(IsotonicRegressionModel) since 2.1.0

summary(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 2.1.3 Index]