pemaCompute: Compute PEMA

Applies to version 8.0.3 of package RevoPemaR.

Description

Use a PemaBaseClass reference class object to perform a Parallel External Memory Algorithm (PEMA) computation.

Usage

  pemaCompute(pemaObj, data = NULL, outData = NULL, overwrite = FALSE, append = "none",
      computeContext = NULL, initPema = TRUE, ...)

Arguments

pemaObj

A PemaBaseClass reference class object containing the methods for the analysis.

data

A data frame or RevoScaleR data source object.

outData

An RevoScaleR data source object that has write capabilities, such as an .xdf file. Not used by all PemaBaseClass reference class objects.

overwrite

logical value. If TRUE, an existing outFile will be overwritten.

append

either "none" to create a new file, "rows" to append rows to an existing file, or "cols" to append columns to an existing file. If outFile exists and append is "none", the overwrite argument must be set to TRUE. Ignored when outData is not specified or not relevant. You cannot append to RxTextData or RxTeradata data sources, and appending is not supported for composite .xdf files or when using the RxHadoopMR compute context.

computeContext

NULL or a RevoScaleR compute context object.

initPema

logical. If TRUE the initialize method for the pemaObj object will be called before performing computations.

...

Other fields in the PemaBaseClass class to be utilized in the analysis.

Details

The pemaCompute function provides a framework for writing parallel, external memory algorithms (PEMAs) that can be run serially on a single computer, and will be automatically parallelized when run on cluster supported by RevoScaleR.

Value

The value returned that returned by the PemaBaseClass processResults method. Note that the reference class PemaBaseClass will be reinitialized at the beginning of the analysis unless initPema is set to TRUE, and will contain updated values upon completion.

See Also

PemaBaseClass, PemaMean, setPemaClass

Examples

 # Instantiate an PemaMean reference class
 meanPemaObj <- PemaMean()
 meanPemaObj # See the initialized values of the fields

 # Compute the mean of Petal.Length from the iris data set
 # Call pemaCompute, specifying the custom analyis object, the data, and additional arg
 pemaCompute(pemaObj = meanPemaObj, data = iris, varName = "Petal.Length")

 meanPemaObj # Note that the reference class object has been updated