EnsemblePCReg: Extensible Package for Principal-Component-Regression-Based Heterogeneous Ensemble Meta-Learning

Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.

Install the latest version of this package by entering the following in R:
install.packages("EnsemblePCReg")
AuthorMansour T.A. Sharabiani, Alireza S. Mahani
Date of publication2016-09-14 18:50:34
MaintainerAlireza S. Mahani <alireza.s.mahani@gmail.com>
LicenseGPL (>= 2)
Version1.1.1

View on CRAN

Functions

epcreg Man page
epcreg.baselearner.control Man page
epcreg.integrator.control Man page
epcreg.load Man page
epcreg.save Man page
plot.epcreg Man page
predict.epcreg Man page
Regression.Integrator.PCR.SelMin.Config-class Man page
Regression.Integrator.PCR.SelMin.FitObj-class Man page
Regression.Sweep.CV.Fit Man page
Regression.Sweep.CV.FitObj-class Man page
Regression.Sweep.Fit-methods Man page
Regression.Sweep.Fit,Regression.Sweep.PCR.Config-method Man page
Regression.Sweep.PCR.Config-class Man page
Regression.Sweep.PCR.FitObj-class Man page
summary.epcreg Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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